The Centre for Internet and Society
http://editors.cis-india.org
These are the search results for the query, showing results 41 to 55.
Sean McDonald - Ebola: A Big Data Disaster
http://editors.cis-india.org/papers/ebola-a-big-data-disaster
<b>We are proud to initiate the CIS Papers series with a fascinating exploration of humanitarian use of big data and its discontents by Sean McDonald, FrontlineSMS, in the context of utilisation of Call Detail Records for public health response during the Ebola crisis in Liberia. The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modeling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.</b>
<p> </p>
<h2>Read</h2>
<h4>Download the paper: <a href="https://github.com/cis-india/papers/raw/master/CIS_Papers_2016.01_Sean-McDonald.pdf">PDF</a>.</h4>
<p> </p>
<h2>Preface</h2>
<p>This study titled “Ebola: A Big Data Disaster” by Sean Martin McDonald, undertaken with support from the Open Society Foundation, Ford Foundation, and Media Democracy Fund, explores the use of Big Data in the form of Call Detail Record (CDR) data in humanitarian crisis.</p>
<p> It discusses the challenges of digital humanitarian coordination in health emergencies like the Ebola outbreak in West Africa, and the marked tension in the debate around experimentation with humanitarian technologies and the impact on privacy. McDonald’s research focuses on the two primary legal and human rights frameworks, privacy and property, to question the impact of unregulated use of CDR’s on human rights. It also highlights how the diffusion of data science to the realm of international development constitutes a genuine opportunity to bring powerful new tools to fight crisis and emergencies.</p>
<p>Analysing the risks of using CDRs to perform migration analysis and contact tracing without user consent, as well as the application of big data to disease surveillance is an important entry point into the debate around use of Big Data for development and humanitarian aid. The paper also raises crucial questions of legal significance about the access to information, the limitation of data sharing, and the concept of proportionality in privacy invasion in the public good. These issues hold great relevance in today's time where big data and its emerging role for development, involving its actual and potential uses as well as harms is under consideration across the world.</p>
<p>The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modelling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.</p>
<p>This study offers an important perspective for us at the Centre for Internet and Society, and our works on Privacy, Big Data, and Big Data for Development, and very productively articulates the risks of adopting solutions to issues important for development without taking into consideration legal implications and the larger impact on human rights. We look forward to continue to critically engage with issues raised by Big Data in the context of human rights and sustainable development, and bring together diverse perspectives on these issues.</p>
<p><em>- Elonnai Hickok, Policy Director, the Centre for Internet and Society</em></p>
<p> </p>
<h2>CIS Papers</h2>
<p>The CIS Papers series publishes open access monographs and discussion pieces that critically contribute to the debates on digital technologies and society. It includes publication of new findings and observations, of work-in-progress, and of critical review of existing materials. These may be authored by researchers at or affiliated to CIS, by external researchers and practitioners, or by a group of discussants. CIS offers editorial support to the selected monographs and discussion pieces. The views expressed, however, are of the authors' alone.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/papers/ebola-a-big-data-disaster'>http://editors.cis-india.org/papers/ebola-a-big-data-disaster</a>
</p>
No publishersumandroBig DataPrivacyOpen DataDisaster ResponseInternet GovernanceHumanitarian ResponseCIS Papers2016-04-21T09:57:26ZBlog EntryAadhaar Bill 2016 & NIAI Bill 2010 - Comparing the Texts
http://editors.cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison
<b>This is a quick comparison of the texts of the Aadhaar Bill 2016 and the National Identification Authority of India Bill 2010. The new sections in the former are highlighed, and the deleted sections (that were part of the latter) are struck out.</b>
<p> </p>
<iframe src="http://cis-india.github.io/aadhaar-bill-2016/" frameborder="0" height="500px" width="100%"> </iframe>
<p> </p>
<p>Source: <a href="http://cis-india.github.io/aadhaar-bill-2016/">http://cis-india.github.io/aadhaar-bill-2016/</a></p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison'>http://editors.cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison</a>
</p>
No publishersumandroUIDAadhaarBig DataPrivacy2016-03-09T11:25:01ZBlog EntryBig Data in India: Benefits, Harms, and Human Rights - Workshop Report
http://editors.cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report
<b>The Centre for Internet and Society held a one-day workshop on “Big Data in India: Benefits, Harms and Human Rights” at India Habitat Centre, New Delhi on the 1st of October, 2016. This report is a compilation of the the issues discussed, ideas exchanged and challenges recognized during the workshop. The objective of the workshop was to discuss aspects of big data technologies in terms of harms, opportunities and human rights. The discussion was designed around an extensive study of current and potential future uses of big data for governance in India, that CIS has undertaken over the last year with support from the MacArthur Foundation.</b>
<p> </p>
<p><strong>Contents</strong></p>
<p><a href="#1"><strong>Big Data: Definitions and Global South Perspectives</strong></a></p>
<p><a href="#2"><strong>Aadhaar as Big Data</strong></a></p>
<p><a href="#3"><strong>Seeding</strong></a></p>
<p><a href="#4"><strong>Aadhaar and Data Security</strong></a></p>
<p><a href="#5"><strong>Aadhaar’s Relational Arrangement with Big Data Scheme</strong></a></p>
<p><a href="#6"><strong>The Myths surrounding Aadhaar</strong></a></p>
<p><a href="#7"><strong>IndiaStack and FinTech Apps</strong></a></p>
<p><a href="#8"><strong>Problems with UID</strong></a></p>
<hr />
<h2 id="1">Big Data: Definitions and Global South Perspectives</h2>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">“Big Data” has been defined by multiple scholars till date. The first consideration at the workshop was to discuss various definitions of big data, and also to understand what could be considered Big Data in terms of governance, especially in the absence of academic consensus. One of the most basic ways to define it, as given by the National Institute of Standards and Technology, USA, is to take it to be the data that is beyond the computational capacity of current systems. This definition has been accepted by the UIDAI of India. Another participant pointed out that Big Data is not only indicative of size, but rather the nature of data which is unstructured, and continuously flowing. The Gartner definition of Big Data relies on the three Vs i.e. Volume (size), Velocity (infinite number of ways in which data is being continuously collected) and Variety (the number of ways in which data can be collected in rows and columns).</p>
<p style="text-align: justify;" dir="ltr">The presentation also looked at ways in which Big Data is different from traditional data. It was pointed out that it can accommodate diverse unstructured datasets, and it is ‘relational’ i.e. it needs the presence of common field(s) across datasets which allows these fields to be conjoined. For e.g., the UID in India is being linked to many different datasets, and they don’t constitute Big Data separately, but do so together. An increasingly popular definition is to define data as “Big Data” based on what can be achieved through it. It has been described by authors as the ability to harness new kinds of insight which can inform decision making. It was pointed out that CIS does not subscribe to any particular definition, and is still in the process of coming up with a comprehensive definition of Big Data.</p>
<p style="text-align: justify;" dir="ltr">Further, discussion touched upon the approach to Big Data in the Global South. It was pointed out that most discussions about Big Data in the Global South are about the kind of value that it can have, the ways in which it can change our society. The Global North, on the other hand, has moved on to discussing the ethics and privacy issues associated with Big Data.</p>
<p style="text-align: justify;" dir="ltr">After this, the presentation focussed on case studies surrounding key Central Government initiatives and projects like Aadhaar, Predictive Policing, and Financial Technology (FinTech).</p>
<h2 id="2">Aadhaar as Big Data</h2>
<p style="text-align: justify;" dir="ltr">In presenting CIS’ case study on Aadhaar, it was pointed out that initially, Aadhaar, with its enrollment dataset was by itself being seen as Big Data. However, upon careful consideration in light of definitions discussed above, it can be seen as something that enables Big Data. The different e-governance projects within Digital India, along with Aadhaar, constitute Big Data. The case study discussed the Big Data implications of Aadhaar, and in particular looked at a ‘cradle to grave’ identity mapping through various e-government projects and the datafication of various transaction generated data.</p>
<h2 id="3">Seeding</h2>
<p style="text-align: justify;" dir="ltr">Any digital identity like Aadhaar typically has three features: 1. Identification i.e. a number or card used to identify yourself; 2. Authentication, which is based on your number or card and any other digital attributes that you might have; 3. Authorisation: As bearers of the digital identity, we can authorise the service providers to take some steps on our behalf. The case study discussed ‘seeding’ which enables the Big Data aspects of Digital India. In the process of seeding, different government databases can be seeded with the UID number using a platform called Ginger. Due to this, other databases can be connected to UIDAI, and through it, data from other databases can be queried by using your Aadhaar identity itself. This is an example of relationality, where fractured data is being brought together. At the moment, it is not clear whether this access by UIDAI means that an actual physical copy of such data from various sources will be transferred to UIDAI’s servers or if they will just access it through internet, but the data remains on the host government agency’s server. An example of even private parties becoming a part of this infrastructure was raised by a participant when it was pointed out that Reliance Jio is now asking for fingerprints. This can then be connected to the relational infrastructure being created by UIDAI. The discussion then focused on how such a structure will function, where it was mentioned that as of now, it cannot be said with certainty that UIDAI will be the agency managing this relational infrastructure in the long run, even though it is the one building it.</p>
<h2 id="4">Aadhaar and Data Security</h2>
<p style="text-align: justify;" dir="ltr">This case study also dealt with the sheer lack of data protection legislation in India except for S.43A of the IT Act. The section does not provide adequate protection as the constitutionality of the rules and regulations under S.43A is ambivalent. More importantly, it only refers to private bodies. Hence, any seeding which is being done by the government is outside the scope of data protection legislation. Thus, at the moment, no legal framework covers the processes and the structures being used for datasets. Due to the inapplicability of S.43A to public bodies, questions were raised as to the existence of a comprehensive data protection policy for government institutions. Participants answered the question in the negative. They pointed out that if any government department starts collecting data, they develop their own privacy policy. There are no set guidelines for such policies and they do not address concerns related to consent, data minimisation and purpose limitation at all. Questions were also raised about the access and control over Big Data with government institutions. A tentative answer from a participant was that such data will remain under the control of the domain specific government ministry or department, for e.g. MNREGA data with the Ministry of Rural Development, because the focus is not on data centralisation but rather on data linking. As long as such fractured data is linked and there is an agency that is responsible to link them, this data can be brought together. Such data is primarily for government agencies. But the government is opening up certain aspects of the data present with it for public consumption for research and entrepreneurial purposes.The UIDAI provides you access to your own data after paying a minimal fee. The procedure for such access is still developing.</p>
<h2 id="5">Aadhaar’s Relational Arrangement with Big Data Scheme</h2>
<p style="text-align: justify;" dir="ltr">The various Digital India schemes brought in by the government were elucidated during the workshop. It was pointed out that these schemes extend to myriad aspects of a citizen’s daily life and cover all the essential public services like health, education etc. This makes Aadhaar imperative even though the Supreme Court has observed that it is not mandatory for every citizen to have a unique identity number. The benefits of such identity mapping and the ecosystem being generated by it was also enumerated during the discourse. But the complete absence of any data ethics or data confidentiality principles make us unaware of the costs at which these benefits are being conferred on us. Apart from surveillance concerns, the knowledge gap being created between the citizens and the government was also flagged. Three main benefits touted to be provided by Aadhaar were then analysed. The first is the efficient delivery of services. This appears to be an overblown claim as the Aadhaar specific digitisation and automation does not affect the way in which employment will be provided to citizens through MNREGA or how wage payment delays will be overcome. These are administrative problems that Aadhaar and associated technologies cannot solve. The second is convenience to the citizens. The fallacies in this assertion were also brought out and identified. Before the Aadhaar scheme was rolled in, ration cards were issued based on certain exclusion and inclusion criteria.. The exclusion and inclusion criteria remain the same while another hurdle in the form of Aadhaar has been created. As India is still lacking in supporting infrastructure such as electricity, server connectivity among other things, Aadhaar is acting as a barrier rather than making it convenient for citizens to enroll in such schemes.The third benefit is fraud management. Here, a participant pointed out that this benefit was due to digitisation in the form of GPS chips in food delivery trucks and electronic payment and not the relational nature of Aadhaar. Aadhaar is only concerned with the linking up or relational part. About deduplication, it was pointed out how various government agencies have tackled it quite successfully by using technology different from biometrics which is unreliable at the best of times.</p>
<h2 id="6">The Myths surrounding Aadhaar</h2>
<p style="text-align: justify;" dir="ltr">The discussion also reflected on the fact that Aadhaar is often considered to be a panacea that subsumes all kinds of technologies to tackle leakages. However, this does not take into account the fact that leakages happen in many ways. A system should have been built to tackle those specific kinds of leakages, but the focus is solely on Aadhaar as the cure for all. Notably, participants who have been a part of the government pointed out how this myth is misleading and should instead be seen as the first step towards a more digitally enhanced country which is combining different technologies through one medium.</p>
<h2 id="7">IndiaStack and FinTech Apps</h2>
<h3 id="71">What is India Stack?</h3>
<p style="text-align: justify;" dir="ltr">The focus then shifted to another extremely important Big Data project, India Stack, being conceptualised and developed by a team of private developers called iStack, for the NPCI. It builds on the UID project, Jan Dhan Yojana and mobile services trinity to propagate and develop a cashless, presence-less, paperless and granular consent layer based on UID infrastructure to digitise India.</p>
<p style="text-align: justify;" dir="ltr">A participant pointed out that the idea of India Stack is to use UID as a platform and keep stacking things on it, such that more and more applications are developed. This in turn will help us to move from being a ‘data poor’ country to a ‘data rich’ one. The economic benefits of this data though as evidenced from the TAGUP report - a report about the creation of National Information Utilities to manage the data that is present with the government - is for the corporations and not the common man. The TAGUP report openly talks about privatisation of data.</p>
<h3 id="72">Problems with India Stack</h3>
<p style="text-align: justify;" dir="ltr">The granular consent layer of India Stack hasn’t been developed yet but they have proposed to base it on MIT Media Lab’s OpenPDS system. The idea being that, on the basis of the choices made by the concerned person, access to a person’s personal information may be granted to an agency like a bank. What is more revolutionary is that India Stack might even revoke this access if the concerned person expresses a wish to do so or the surrounding circumstances signal to India Stack that it will be prudent to do so. It should be pointed out that the the technology required for OpenPDS is extremely complex and is not available in India. Moreover, it’s not clear how this system would work. Apart from this, even the paperless layer has its faults and has been criticised by many since its inception, because an actual government signed and stamped paper has been the basis of a claim.. In the paperless system, you are provided a Digilocker in which all your papers are stored electronically, on the basis of your UID number. However, it was brought to light that this doesn’t take into account those who either do not want a Digilocker or UID number or cases where they do not have access to their digital records. How in such cases will people make claims?</p>
<h3 id="73">A Digital Post-Dated Cheque: It’s Ramifications</h3>
<p style="text-align: justify;" dir="ltr">A key change that FinTech apps and the surrounding ecosystem want to make is to create a digital post-dated cheque so as to allow individuals to get loans from their mobiles especially in remote areas. This will potentially cut out the need to construct new banks, thus reducing the capital expenditure , while at the same time allowing the credit services to grow. The direct transfer of money between UID numbers without the involvement of banks is a step to further help this ecosystem grow. Once an individual consents to such a system, however, automatic transfer of money from one’s bank accounts will be affected, regardless of the reason for payment. This is different from auto debt deductions done by banks presently, as in the present system banks have other forms of collateral as well. The automatic deduction now is only affected if these other forms are defaulted upon. There is no knowledge as to whether this consent will be reversible or irreversible. As Jan Dhan Yojana accounts are zero balance accounts, the account holder will be bled dry. The implication of schemes such as “Loan in under 8 minutes” were also discussed. The advantage of such schemes is that transaction costs are reduced.The financial institution can thus grant loans for the minimum amount without any additional enquiries. It was pointed out that this new system is based on living on future income much like the US housing bubble crash. Interestingly, in Public Distribution Systems, biometrics are insisted upon even though it disrupts the system. This can be seen as a part of the larger infrastructure to ensure that digital post-dated cheques become a success.</p>
<h3 id="74">The Role of FinTech Apps</h3>
<p style="text-align: justify;" dir="ltr">FinTech ‘apps’ are being presented with the aim of propagating financial inclusion. The Technology Advisory Group for Unique Projects report stated that as managing such information sources is a big task, just like electricity utilities, a National Information Utilities (NIU) should be set up for data sources. These NIUs as per the report will follow a fee based model where they will be charging for their services for government schemes. The report identified two key NIUs namely the National Payments Corporation of India (NPCI) and the Goods and Services Tax Network (GSTN). The key usage that FinTech applications will serve is credit scoring. The traditional credit scoring data sources only comprised a thin file of records for an individual, but the data that FinTech apps collect - a person’s UID number, mobile number. and bank account number all linked up, allow for a far more comprehensive credit rating. Government departments are willing to share this data with FinTech apps as they are getting analysis in return. Thus, by using UID and the varied data sources that have been linked together by UID, a ‘thick file’ is now being created by FinTech apps. Banking apps have not yet gone down the route of FinTech apps to utilise Big Data for credit scoring purposes.</p>
<p style="text-align: justify;" dir="ltr"> </p>
<p style="text-align: justify;" dir="ltr">The two main problems with such apps is that there is no uniform way of credit scoring. This distorts the rate at which a person has to pay interest. The consent layer adds another layer of complication as refusal to share mobile data with a FinTech app may lead to the app declaring one to be a risky investment thus, subjecting that individual to a higher rate of interest .</p>
<div style="text-align: justify;" dir="ltr"> </div>
<h3 id="75">Regulation of FinTech Apps and the UID Infrastructure</h3>
<p style="text-align: justify;" dir="ltr"> India Stack and the applications that are being built on it, generate a lot of transaction metadata that is very intimate in nature. The privacy aspects of the UID legislation doesn't cover such data. The granular consent layer which has been touted to cover this still has to come into existence. Also, Big Data is based on sharing and linking of data. Here, privacy concerns and Big Data objectives clash. Big Data by its very nature challenges privacy principles like data minimisation and purpose limitation.The need for regulation to cover the various new apps and infrastructure which are being developed was pointed out.</p>
<h2 id="8">Problems with UID</h2>
<p style="text-align: justify;" dir="ltr">It has been observed that any problem present with Aadhaar is usually labelled as a teething problem, it’s claimed that it will be solved in the next 10 years. But, this begs the question - why is the system online right now?</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Aadhaar is essentially a new data condition and a new exclusion or inclusion criteria. Data exclusion modalities as observed in Rajasthan after the introduction of biometric Point of Service (POS) machines at ration shops was found to be 45% of the population availing PDS services. This number also includes those who were excluded from the database by being included in the wrong dataset. There is no information present to tell us how many actual duplicates and how many genuine ration card holders were weeded out/excluded by POS.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">It was also mentioned that any attempt to question Aadhaar is considered to be an attempt to go back to the manual system and this binary thinking needs to change. Big Data has the potential to benefit people, as has been evidenced by the scholarship and pension portals. However, Big Data’s problems arise in systems like PDS, where there is centralised exclusion at the level of the cloud. Moreover, the quantity problem present in the PDS and MNREGA systems persists. There is still the possibility of getting lesser grains and salary even with analysis of biometrics, hence proving that there are better technologies to tackle these problems. Presently, the accountability mechanisms are being weakened as the poor don’t know where to go to for redressal. Moreover, the mechanisms to check whether the people excluded are duplicates or not is not there. At the time of UID enrollment, out of 90 crores, 9 crore were rejected. There was no feedback or follow-up mechanism to figure out why are people being rejected. It was just assumed that they might have been duplicates.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Another problem is the rolling out of software without checking for inefficiencies or problems at a beta testing phase. The control of developers over this software, is so massive that it can be changed so easily without any accountability.. The decision making components of the software are all proprietary like in the the de-duplication algorithm being used by the UIDAI. Thus, this leads to a loss of accountability because the system itself is in flux, none of it is present in public domain and there are no means to analyse it in a transparent fashion..</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">These schemes are also being pushed through due to database politics. On a field study of NPR of citizens, another Big Data scheme, it was found that you are assumed to be an alien if you did not have the documents to prove that you are a citizen. Hence, unless you fulfill certain conditions of a database, you are excluded and are not eligible for the benefits that being on the database afford you.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Why is the private sector pushing for UIDAI and the surrounding ecosystem?</p>
<p style="text-align: justify;" dir="ltr">Financial institutions stand to gain from encouraging the UID as it encourages the credit culture and reduces transaction costs.. Another advantage for the private sector is perhaps the more obvious one, that is allows for efficient marketing of products and services..</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">The above mentioned fears and challenges were actually observed on the ground and the same was shown through the medium of a case study in West Bengal on the smart meters being installed there by the state electricity utility. While the data coming in from these smart meters is being used to ensure that a more efficient system is developed,it is also being used as a surrogate for income mapping on the basis of electricity bills being paid. This helps companies profile neighbourhoods. The technical officer who first receives that data has complete control over it and he can easily misuse the data. This case study again shows that instruments like Aadhaar and India Stack are limited in their application and aren’t the panacea that they are portrayed to be.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">A participant pointed out that in the light of the above discussions, the aim appears to be to get all kinds of data, through any source, and once you have gotten the UID, you link all of this data to the UID number, and then use it in all the corporate schemes that are being started. Most of the problems associated with Big Data are being described as teething problems. The India Stack and FinTech scheme is coming in when we already know about the problems being faced by UID. The same problems will be faced by India Stack as well.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Can you opt out of the Aadhaar system and the surrounding ecosystem?</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">The discussion then turned towards whether there can be voluntary opting out from Aadhaar. It was pointed out that the government has stated that you cannot opt out of Aadhaar. Further, the privacy principles in the UIDAI bill are ambiguously worded where individuals only have recourse for basic things like correction of your personal information. The enforcement mechanism present in the UIDAI Act is also severely deficient. There is no notification procedure if a data breach occurs. . The appellate body ‘Cyber Appellate Tribunal’ has not been set up in three years.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">CCTNS: Big Data and its Predictive Uses</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">What is Predictive Policing?</p>
<p style="text-align: justify;" dir="ltr">The next big Big Data case study was on the Crime and Criminal Tracking Network & Systems (CCTNS). Originally it was supposed to be a digitisation and interconnection scheme where police records would be digitised and police stations across the length and breadth of the country would be interconnected. But, in the last few years some police departments of states like Chandigarh, Delhi and Jharkhand have mooted the idea of moving on to predictive policing techniques. It envisages the use of existing statistical and actuarial techniques along with many other tropes of data to do so. It works in four ways: 1. By predicting the place and time where crimes might occur; 2. To predict potential future offenders; 3. To create profiles of past crimes in order to predict future crimes; 4. Predicting groups of individuals who are likely to be victims of future crimes.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">How is Predictive Policing done?</p>
<p style="text-align: justify;" dir="ltr">To achieve this, the following process is followed: 1. Data collection from various sources which includes structured data like FIRs and unstructured data like call detail records, neighbourhood data, crime seasonal patterns etc. 2. Analysis by using theories like the near repeat theory, regression models on the basis of risk factors etc. 3. Intervention</p>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Flaws in Predictive Policing and questions of bias</p>
<p style="text-align: justify;" dir="ltr">An obvious weak point in the system is that if the initial data going into the system is wrong or biased, the analysis will also be wrong. Efforts are being made to detect such biases. An important way to do so will be by building data collection practices into the system that protect its accuracy. The historical data being entered into the system is carrying on the prejudices inherited from the British Raj and biases based on religion, caste, socio-economic background etc.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">One participant brought about the issue of data digitization in police stations, and the impact of this haphazard, unreliable data on a Big Data system. This coupled with paucity of data is bound to lead to arbitrary results. An effective example was that of black neighbourhoods in the USA. These are considered problematic and thus they are policed more, leading to a higher crime rate as they are arrested for doing things that white people in an affluent neighbourhood get away with. This in turn further perpetuates the crime rate and it becomes a self-fulfilling prophecy. In India, such a phenomenon might easily develop in the case of migrants, de-notified tribes, Muslims etc. A counter-view on bias and discrimination was offered here. One participant pointed out that problems with haphazard or poor quality of data is not a colossal issue as private companies are willing to fill this void and are actually doing so in exchange for access to this raw data. It was also pointed out how bias by itself is being used as an all encompassing term. There are multiplicities of biases and while analysing the data, care should be taken to keep it in mind that one person’s bias and analysis might and usually does differ from another. Even after a computer has analysed the data, the data still falls into human hands for implementation.</p>
<p style="text-align: justify;" dir="ltr">The issue of such databases being used to target particular communities on the basis of religion, race, caste, ethnicity among other parameters was raised. Questions about control and analysis of data were also discussed, i.e. whether it will be top-down with data analysis being done in state capitals or will this analysis be done at village and thana levels as well too. It was discussed as topointed out how this could play a major role in the success and possible persecutory treatment of citizens, as the policemen at both these levels will have different perceptions of what the data is saying. . It was further pointed out, that at the moment, there’s no clarity on the mode of implementation of Big Data policing systems. Police in the USA have been seen to rely on Big Data so much that they have been seen to become ‘data myopic’. For those who are on the bad side of Big Data, in the Indian context, laws like preventive detention can be heavily misused.There’s a very high chance that predictive policing due to the inherent biases in the system and the prejudices and inefficiency of the legal system will further suppress the already targeted sections of the society. A counterpoint was raised and it was suggested that contrary to our fears, CCTNS might lead to changes in our understanding and help us to overcome longstanding biases.</p>
<p style="text-align: justify;" dir="ltr">Open Knowledge Architecture as a solution to Big Data biases?</p>
<p style="text-align: justify;" dir="ltr">The conference then mulled over the use of ‘Open Knowledge’ architecture to see whether it can provide the solution to rid Big Data of its biases and inaccuracies if enough eyes are there. It was pointed out that Open Knowledge itself can’t provide foolproof protection against these biases as the people who make up the eyes themselves are predominantly male belonging to the affluent sections of the society and they themselves suffer from these biases.</p>
<p style="text-align: justify;" dir="ltr">Who exactly is Big Data supposed to serve?</p>
<p style="text-align: justify;" dir="ltr">The discussion also looked at questions such as who is this data for? Janata Information System (JIS), is a concept developed by MKSS where the data collected and generated by the government is taken to be for the common citizens. For e.g. MNREGA data should be used to serve the purposes of the labourers. The raw data as is available at the moment, usually cannot be used by the common man as it is so vast and full of information that is not useful for them at all. It was pointed out that while using Big Data for policy planning purposes, the actual string of information that turned out to be needed was very little but the task of unravelling this data for civil society purposes is humongous. By presenting the data in the right manner, the individual can be empowered. The importance of data presentation was also flagged. It was agreed upon that the content of the data should be for the labourer and not a MNC, as the MNC has the capability to utilise the raw data on it’s own regardless.</p>
<p style="text-align: justify;" dir="ltr">Concerns about Big Data usage</p>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Participants pointed out that privacy concerns are usually brushed under the table due to a belief that the law is sufficient or that the privacy battle has already been lost. </p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">In the absence of knowledge of domain and context, Big Data analysis is quite limited. Big Data’s accuracy and potential to solve problems needs to be factually backed.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The narrative of Big Data often rests on the assumption that descriptive statistics take over inferential statistics, thus eliminating the need for domain specific knowledge. It is claimed that the data is so big that it will describe everything that we need to know.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Big Data is creating a shift from a deductive model of scientific rigour to an inductive one. In response to this, a participant offered the idea that troves of good data allow us to make informed questions on the basis of which the deductive model will be formed. A hybrid approach combining both deductive and inductive might serve us best.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The need to collect the right data in the correct format, in the right place was also expressed.</p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Potential Research Questions & Participants’ Areas of Research</p>
<p style="text-align: justify;" dir="ltr">Following this discussion, participants brainstormed to come up with potential areas of research and research questions. They have been captured below:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Big Data, Aadhaar and India Stack:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Has Aadhaar been able to tackle illegal ways of claiming services or are local negotiations and other methods still prevalent?</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Is the consent layer of India Stack being developed in a way that provides an opportunity to the UID user to give informed consent? The OpenPDS and its counterpart in the EU i.e. the My Data Structure were designed for countries with strong privacy laws. Importantly, they were meant for information shared on social media and not for an individual’s health or credit history. India is using it in a completely different sphere without strong data protection laws. What were the granular consent layer structures present in the West designed for and what were they supposed to protect?</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The question of ownership of data needs to be studied especially in context of a globalised world where MNCs are collecting copious amounts of data of Indian citizens. What is the interaction of private parties in this regard?</p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Big Data and Predictive Policing:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">How are inequalities being created through the Big Data systems? Lessons should be taken from the Western experience with the advent of predictive policing and other big data techniques - they tend to lead to perpetuation of the current biases which are already ingrained in the system.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">It was also pointed out how while studying these topics and anything related to technology generally, we become aware of a divide that is present between the computational sciences and social sciences. This divide needs to be erased if Big Data or any kind of data is to be used efficiently. There should be a cross-pollination between different groups of academics. An example of this can be seen to be the ‘computational social sciences departments’ that have been coming up in the last 3-4 years.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Why are so many interim promises made by Big Data failing? A study of this phenomenon needs to be done from a social science perspective. This will allow one to look at it from a different angle.</p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Studying Big Data:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">What is the historical context of the terms of reference being used for Big Data? The current Big Data debate in India is based on parameters set by the West. For better understanding of Big Data, it was suggested that P.C. Mahalanobis’ experience while conducting the Indian census, (which was the Big Data of that time) can be looked at to get a historical perspective on Big Data. This comparison might allow us to discover questions that are important in the Indian context. It was also suggested that rather than using ‘Big Data’ as a catchphrase to describe these new technological innovations, we need to be more discerning.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">What are the ideological aspects that must be considered while studying Big Data? What does the dialectical promise of technology mean? It was contended that every time there is a shift in technology, the zeitgeist of that period is extremely excited and there are claims that it will solve everything. There’s a need to study this dialectical promise and the social promise surrounding it.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Apart from the legitimate fears that Big Data might lead to exclusion, what are the possibilities in which it improve inclusion too?</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The diminishing barrier between the public and private self, which is a tangent to the larger public-private debate was mentioned.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">How does one distinguish between technology failure and process failure while studying Big Data? </p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Big Data: A Friend?</p>
<p style="text-align: justify;" dir="ltr">In the concluding session, the fact that the Big Data moment cannot be wished away was acknowledged. The use of analytics and predictive modelling by the private sector is now commonplace and India has made a move towards a database state through UID and Digital India. The need for a nuanced debate, that does away with the false equivalence of being either a Big Data enthusiast or a luddite is crucial.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">A participant offered two approaches to solving a Big Data problem. The first was the Big Data due process framework which states that if a decision has been taken that impacts the rights of a citizen, it needs to be cross examined. The efficacy and practicality of such an approach is still not clear. The second, slightly paternalistic in nature, was the approach where Big Data problems would be solved at the data science level itself. This is much like the affirmative algorithmic approach which says that if in a particular dataset, the data for the minority community is not available then it should be artificially introduced in the dataset. It was also suggested that carefully calibrated free market competition can be used to regulate Big Data. For e.g. a private personal wallet company that charges higher, but does not share your data at all can be an example of such competition. </p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Another important observation was the need to understand Big Data in a Global South context and account for unique challenges that arise. While the convenience of Big Data is promising, its actual manifestation depends on externalities like connectivity, accurate and adequate data etc that must be studied in the Global South.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">While the promises of Big Data are encouraging, it is also important to examine its impacts and its interaction with people's rights. Regulatory solutions to mitigate the harms of big data while also reaping its benefits need to evolve.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<p><span id="docs-internal-guid-90fa226f-6157-27d9-30cd-050bdc280875"></span></p>
<div style="text-align: justify;" dir="ltr"> </div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report'>http://editors.cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report</a>
</p>
No publisherVidushi Marda, Akash Deep Singh and Geethanjali JujjavarapuHuman RightsUIDBig DataPrivacyArtificial IntelligenceInternet GovernanceMachine LearningFeaturedDigital IndiaAadhaarInformation TechnologyE-Governance2016-11-18T12:58:19ZBlog EntrySummary Report Internet Governance Forum 2015
http://editors.cis-india.org/internet-governance/blog/summary-report-internet-governance-forum-2015
<b>Centre for Internet and Society (CIS), India participated in the Internet Governance Forum (IGF) held at Poeta Ronaldo Cunha Lima Conference Center, Joao Pessoa in Brazil from 10 November 2015 to 13 November 2015. The theme of IGF 2015 was ‘Evolution of Internet Governance: Empowering Sustainable Development’. Sunil Abraham, Pranesh Prakash & Jyoti Panday from CIS actively engaged and made substantive contributions to several key issues affecting internet governance at the IGF 2015. The issue-wise detail of their engagement is set out below. </b>
<p align="center" style="text-align: left;"><strong>INTERNET
GOVERNANCE</strong></p>
<p align="justify">
I. The
Multi-stakeholder Advisory Group to the IGF organised a discussion on
<em><strong>Sustainable
Development Goals (SDGs) and Internet Economy</strong></em><em>
</em>at
the Main Meeting Hall from 9:00 am to 12:30 pm on 11 November, 2015.
The
discussions at this session focused on the importance of Internet
Economy enabling policies and eco-system for the fulfilment of
different SDGs. Several concerns relating to internet
entrepreneurship, effective ICT capacity building, protection of
intellectual property within and across borders were availability of
local applications and content were addressed. The panel also
discussed the need to identify SDGs where internet based technologies
could make the most effective contribution. Sunil
Abraham contributed to the panel discussions by addressing the issue
of development and promotion of local content and applications. List
of speakers included:</p>
<ol>
<li>
<p align="justify">
Lenni
Montiel, Assistant-Secretary-General for Development, United Nations</p>
</li><li>
<p align="justify">
Helani
Galpaya, CEO LIRNEasia</p>
</li><li>
<p align="justify">
Sergio
Quiroga da Cunha, Head of Latin America, Ericsson</p>
</li><li>
<p align="justify">
Raúl
L. Katz, Adjunct Professor, Division of Finance and Economics,
Columbia Institute of Tele-information</p>
</li><li>
<p align="justify">
Jimson
Olufuye, Chairman, Africa ICT Alliance (AfICTA)</p>
</li><li>
<p align="justify">
Lydia
Brito, Director of the Office in Montevideo, UNESCO</p>
</li><li>
<p align="justify">
H.E.
Rudiantara, Minister of Communication & Information Technology,
Indonesia</p>
</li><li>
<p align="justify">
Daniel
Sepulveda, Deputy Assistant Secretary, U.S. Coordinator for
International and Communications Policy at the U.S. Department of
State </p>
</li><li>
<p align="justify">
Deputy
Minister Department of Telecommunications and Postal Services for
the republic of South Africa</p>
</li><li>
<p align="justify">
Sunil
Abraham, Executive Director, Centre for Internet and Society, India</p>
</li><li>
<p align="justify">
H.E.
Junaid Ahmed Palak, Information and Communication Technology
Minister of Bangladesh</p>
</li><li>
<p align="justify">
Jari
Arkko, Chairman, IETF</p>
</li><li>
<p align="justify">
Silvia
Rabello, President, Rio Film Trade Association</p>
</li><li>
<p align="justify">
Gary
Fowlie, Head of Member State Relations & Intergovernmental
Organizations, ITU</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">http</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">://</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">www</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">.</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">intgovforum</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">.</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">org</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">/</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">cms</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">/</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">igf</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">2015-</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">main</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">-</a><a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">sessions</a><u>
</u></p>
<p align="justify">
Transcript
of the workshop is available here
<u><a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2327-2015-11-11-internet-economy-and-sustainable-development-main-meeting-room">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2327-2015-11-11-internet-economy-and-sustainable-development-main-meeting-room</a></u></p>
<p align="justify">
Video
link Internet
economy and Sustainable Development here
<a href="https://www.youtube.com/watch?v=D6obkLehVE8">https://www.youtube.com/watch?v=D6obkLehVE8</a></p>
<p align="justify"> II.
Public
Knowledge organised a workshop on <em><strong>The
Benefits and Challenges of the Free Flow of Data </strong></em>at
Workshop Room
5 from 11:00 am to 12:00 pm on 12 November, 2015. The discussions in
the workshop focused on the benefits and challenges of the free flow
of data and also the concerns relating to data flow restrictions
including ways to address
them. Sunil
Abraham contributed to the panel discussions by addressing the issue
of jurisdiction of data on the internet. The
panel for the workshop included the following.</p>
<ol>
<li>
<p align="justify">
Vint
Cerf, Google</p>
</li><li>
<p align="justify">
Lawrence
Strickling, U.S. Department of Commerce, NTIA</p>
</li><li>
<p align="justify">
Richard
Leaning, European Cyber Crime Centre (EC3), Europol</p>
</li><li>
<p align="justify">
Marietje
Schaake, European Parliament</p>
</li><li>
<p align="justify">
Nasser
Kettani, Microsoft</p>
</li><li>
<p align="justify">
Sunil
Abraham, CIS
India</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">http</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">://</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">www</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">.</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">intgovforum</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">.</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">org</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">/</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">cms</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">/</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">workshops</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">/</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">list</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">of</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">published</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">workshop</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">proposals</a><u>
</u></p>
<p align="justify">
Transcript
of the workshop is available here
<a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2467-2015-11-12-ws65-the-benefits-and-challenges-of-the-free-flow-of-data-workshop-room-5">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2467-2015-11-12-ws65-the-benefits-and-challenges-of-the-free-flow-of-data-workshop-room-5</a></p>
<p align="justify">
Video link https://www.youtube.com/watch?v=KtjnHkOn7EQ</p>
<p align="justify"> III.
Article
19 and
Privacy International organised a workshop on <em><strong>Encryption
and Anonymity: Rights and Risks</strong></em>
at Workshop Room 1 from 11:00 am to 12:30 pm on 12 November, 2015.
The
workshop fostered a discussion about the latest challenges to
protection of anonymity and encryption and ways in which law
enforcement demands could be met while ensuring that individuals
still enjoyed strong encryption and unfettered access to anonymity
tools. Pranesh
Prakash contributed to the panel discussions by addressing concerns
about existing south Asian regulatory framework on encryption and
anonymity and emphasizing the need for pervasive encryption. The
panel for this workshop included the following.</p>
<ol>
<li>
<p align="justify">
David
Kaye, UN Special Rapporteur on Freedom of Expression</p>
</li><li>
<p align="justify">
Juan
Diego Castañeda, Fundación Karisma, Colombia</p>
</li><li>
<p align="justify">
Edison
Lanza, Organisation of American States Special Rapporteur</p>
</li><li>
<p align="justify">
Pranesh
Prakash, CIS India</p>
</li><li>
<p align="justify">
Ted
Hardie, Google</p>
</li><li>
<p align="justify">
Elvana
Thaci, Council of Europe</p>
</li><li>
<p align="justify">
Professor
Chris Marsden, Oxford Internet Institute</p>
</li><li>
<p align="justify">
Alexandrine
Pirlot de Corbion, Privacy International</p>
</li></ol>
<p align="justify"><a name="_Hlt435412531"></a>
Detailed
description of the workshop is available here
<a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">http</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">://</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">www</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">.</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">intgovforum</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">.</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">org</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">/</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">cms</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">/</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">worksh</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">o</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">ps</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">/</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">list</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">of</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">published</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">workshop</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">-</a><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">proposals</a><u>
</u></p>
<p align="justify">
Transcript
of the workshop is available here
<a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2407-2015-11-12-ws-155-encryption-and-anonymity-rights-and-risks-workshop-room-1">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2407-2015-11-12-ws-155-encryption-and-anonymity-rights-and-risks-workshop-room-1</a></p>
<p align="justify">
Video link available here https://www.youtube.com/watch?v=hUrBP4PsfJo</p>
<p align="justify"> IV.
Chalmers
& Associates organised a session on <em><strong>A
Dialogue on Zero Rating and Network Neutrality</strong></em>
at the Main Meeting Hall from 2:00 pm to 4:00 pm on 12 November,
2015. The Dialogue provided access to expert insight on zero-rating
and a full spectrum of diverse
views on this issue. The Dialogue also explored alternative
approaches to zero rating such as use of community networks. Pranesh
Prakash provided
a
detailed explanation of harms and benefits related to different
approaches to zero-rating. The
panellists for this session were the following.</p>
<ol>
<li>
<p align="justify">
Jochai
Ben-Avie, Senior Global Policy Manager, Mozilla, USA</p>
</li><li>
<p align="justify">
Igor
Vilas Boas de Freitas, Commissioner, ANATEL, Brazil</p>
</li><li>
<p align="justify">
Dušan
Caf, Chairman, Electronic Communications Council, Republic of
Slovenia</p>
</li><li>
<p align="justify">
Silvia
Elaluf-Calderwood, Research Fellow, London School of Economics,
UK/Peru</p>
</li><li>
<p align="justify">
Belinda
Exelby, Director, Institutional Relations, GSMA, UK</p>
</li><li>
<p align="justify">
Helani
Galpaya, CEO, LIRNEasia, Sri Lanka</p>
</li><li>
<p align="justify">
Anka
Kovacs, Director, Internet Democracy Project, India</p>
</li><li>
<p align="justify">
Kevin
Martin, VP, Mobile and Global Access Policy, Facebook, USA</p>
</li><li>
<p align="justify">
Pranesh
Prakash, Policy Director, CIS India</p>
</li><li>
<p align="justify">
Steve
Song, Founder, Village Telco, South Africa/Canada</p>
</li><li>
<p align="justify">
Dhanaraj
Thakur, Research Manager, Alliance for Affordable Internet, USA/West
Indies</p>
</li><li>
<p align="justify">
Christopher
Yoo, Professor of Law, Communication, and Computer & Information
Science, University of Pennsylvania, USA</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<a href="http://www.intgovforum.org/cms/igf2015-main-sessions" target="_top">http://www.intgovforum.org/cms/igf2015-main-sessions</a></p>
<p align="justify">
Transcript
of the workshop is available here
<a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2457-2015-11-12-a-dialogue-on-zero-rating-and-network-neutrality-main-meeting-hall-2">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2457-2015-11-12-a-dialogue-on-zero-rating-and-network-neutrality-main-meeting-hall-2</a></p>
<p align="justify"> V.
The
Internet & Jurisdiction Project organised a workshop on
<em><strong>Transnational
Due Process: A Case Study in MS Cooperation</strong></em>
at Workshop Room
4 from 11:00 am to 12:00 pm on 13 November, 2015. The
workshop discussion focused on the challenges in developing an
enforcement framework for the internet that guarantees transnational
due process and legal interoperability. The discussion also focused
on innovative approaches to multi-stakeholder cooperation such as
issue-based networks, inter-sessional work methods and transnational
policy standards. The panellists for this discussion were the
following.</p>
<ol>
<li>
<p align="justify">
Anne
Carblanc Head of Division, Directorate for Science, Technology and
Industry, OECD</p>
</li><li>
<p align="justify">
Eileen
Donahoe Director Global Affairs, Human Rights Watch</p>
</li><li>
<p align="justify">
Byron
Holland President and CEO, CIRA (Canadian ccTLD)</p>
</li><li>
<p align="justify">
Christopher
Painter Coordinator for Cyber Issues, US Department of State</p>
</li><li>
<p align="justify">
Sunil
Abraham Executive Director, CIS India</p>
</li><li>
<p align="justify">
Alice
Munyua Lead dotAfrica Initiative and GAC representative, African
Union Commission</p>
</li><li>
<p align="justify">
Will
Hudsen Senior Advisor for International Policy, Google</p>
</li><li>
<p align="justify">
Dunja
Mijatovic Representative on Freedom of the Media, OSCE</p>
</li><li>
<p align="justify">
Thomas
Fitschen Director for the United Nations, for International
Cooperation against Terrorism and for Cyber Foreign Policy, German
Federal Foreign Office</p>
</li><li>
<p align="justify">
Hartmut
Glaser Executive Secretary, Brazilian Internet Steering Committee</p>
</li><li>
<p align="justify">
Matt
Perault, Head of Policy Development Facebook</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals">http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals</a></p>
<p align="justify">
Transcript
of the workshop is available here
<a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2475-2015-11-13-ws-132-transnational-due-process-a-case-study-in-ms-cooperation-workshop-room-4">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2475-2015-11-13-ws-132-transnational-due-process-a-case-study-in-ms-cooperation-workshop-room-4</a></p>
<p align="justify">
Video
link Transnational
Due Process: A Case Study in MS Cooperation available here <a href="https://www.youtube.com/watch?v=M9jVovhQhd0">https://www.youtube.com/watch?v=M9jVovhQhd0</a></p>
<p align="justify"> VI.
The Internet Governance Project organised a meeting of the
<em><strong>Dynamic
Coalition on Accountability of Internet Governance Venues</strong></em>
at Workshop Room 2 from 14:00
– 15:30 on
12 November, 2015. The coalition
brought together panelists to highlight the
challenges in developing an accountability
framework
for internet governance
venues that include setting up standards and developing a set of
concrete criteria. Jyoti Panday provided the perspective of civil
society on why acountability is necessary in internet governance
processes and organizations. The panelists for this workshop included
the following.</p>
<ol>
<li>
<p>
Robin
Gross, IP Justice</p>
</li><li>
<p>
Jeanette
Hofmann, Director
<a href="http://www.internetundgesellschaft.de/">Alexander
von Humboldt Institute for Internet and Society</a></p>
</li><li>
<p>
Farzaneh
Badiei,
Internet Governance Project</p>
</li><li>
<p>
Erika
Mann,
Managing
Director Public PolicyPolicy Facebook and Board of Directors
ICANN</p>
</li><li>
<p>
Paul
Wilson, APNIC</p>
</li><li>
<p>
Izumi
Okutani, Japan
Network Information Center (JPNIC)</p>
</li><li>
<p>
Keith
Drazek , Verisign</p>
</li><li>
<p>
Jyoti
Panday,
CIS</p>
</li><li>
<p>
Jorge
Cancio,
GAC representative</p>
</li></ol>
<p>
Detailed
description of the workshop is available here
<a href="http://igf2015.sched.org/event/4c23/dynamic-coalition-on-accountability-of-internet-governance-venues?iframe=no&w=&sidebar=yes&bg=no">http://igf2015.sched.org/event/4c23/dynamic-coalition-on-accountability-of-internet-governance-venues?iframe=no&w=&sidebar=yes&bg=no</a></p>
<p>
Video
link https://www.youtube.com/watch?v=UIxyGhnch7w</p>
<p> VII.
Digital
Infrastructure
Netherlands Foundation organized an open forum at
Workshop Room 3
from 11:00
– 12:00
on
10
November, 2015. The open
forum discussed the increase
in government engagement with “the internet” to protect their
citizens against crime and abuse and to protect economic interests
and critical infrastructures. It
brought
together panelists topresent
ideas about an agenda for the international protection of ‘the
public core of the internet’ and to collect and discuss ideas for
the formulation of norms and principles and for the identification of
practical steps towards that goal.
Pranesh Prakash participated in the e open forum. Other speakers
included</p>
<ol>
<li>
<p>
Bastiaan
Goslings AMS-IX, NL</p>
</li><li>
<p>
Pranesh
Prakash CIS, India</p>
</li><li>
<p>
Marilia
Maciel (FGV, Brasil</p>
</li><li>
<p>
Dennis
Broeders (NL Scientific Council for Government Policy)</p>
</li></ol>
<p>
Detailed
description of the open
forum is available here
<a href="http://schd.ws/hosted_files/igf2015/3d/DINL_IGF_Open%20Forum_The_public_core_of_the_internet.pdf">http://schd.ws/hosted_files/igf2015/3d/DINL_IGF_Open%20Forum_The_public_core_of_the_internet.pdf</a></p>
<p>
Video
link available here <a href="https://www.youtube.com/watch?v=joPQaMQasDQ">https://www.youtube.com/watch?v=joPQaMQasDQ</a></p>
<p>
VIII.
UNESCO, Council of Europe, Oxford University, Office of the High
Commissioner on Human Rights, Google, Internet Society organised a
workshop on hate speech and youth radicalisation at Room 9 on
Thursday, November 12. UNESCO shared the initial outcome from its
commissioned research on online hate speech including practical
recommendations on combating against online hate speech through
understanding the challenges, mobilizing civil society, lobbying
private sectors and intermediaries and educating individuals with
media and information literacy. The workshop also discussed how to
help empower youth to address online radicalization and extremism,
and realize their aspirations to contribute to a more peaceful and
sustainable world. Sunil Abraham provided his inputs. Other speakers
include</p>
<p>
1.
Chaired by Ms Lidia Brito, Director for UNESCO Office in Montevideo</p>
<p>
2.Frank
La Rue, Former Special Rapporteur on Freedom of Expression</p>
<p>
3.
Lillian Nalwoga, President ISOC Uganda and rep CIPESA, Technical
community</p>
<p>
4.
Bridget O’Loughlin, CoE, IGO</p>
<p>
5.
Gabrielle Guillemin, Article 19</p>
<p>
6.
Iyad Kallas, Radio Souriali</p>
<p>
7.
Sunil Abraham executive director of Center for Internet and Society,
Bangalore, India</p>
<p>
8.
Eve Salomon, global Chairman of the Regulatory Board of RICS</p>
<p>
9.
Javier Lesaca Esquiroz, University of Navarra</p>
<p>
10.
Representative GNI</p>
<p>
11.
Remote Moderator: Xianhong Hu, UNESCO</p>
<p>
12.
Rapporteur: Guilherme Canela De Souza Godoi, UNESCO</p>
<p>
Detailed
description of the workshop
is available here
<a href="http://igf2015.sched.org/event/4c1X/ws-128-mitigate-online-hate-speech-and-youth-radicalisation?iframe=no&w=&sidebar=yes&bg=no">http://igf2015.sched.org/event/4c1X/ws-128-mitigate-online-hate-speech-and-youth-radicalisation?iframe=no&w=&sidebar=yes&bg=no</a></p>
<p>
Video
link to the panel is available here
<a href="https://www.youtube.com/watch?v=eIO1z4EjRG0">https://www.youtube.com/watch?v=eIO1z4EjRG0</a></p>
<p> <strong>INTERMEDIARY
LIABILITY</strong></p>
<p align="justify">
IX.
Electronic
Frontier Foundation, Centre for Internet Society India, Open Net
Korea and Article 19 collaborated to organize
a workshop on the <em><strong>Manila
Principles on Intermediary Liability</strong></em>
at Workshop Room 9 from 11:00 am to 12:00 pm on 13 November 2015. The
workshop elaborated on the Manila
Principles, a high level principle framework of best practices and
safeguards for content restriction practices and addressing liability
for intermediaries for third party content. The
workshop
saw particpants engaged in over lapping projects considering
restriction practices coming togetehr to give feedback and highlight
recent developments across liability regimes. Jyoti
Panday laid down the key details of the Manila Principles framework
in this session. The panelists for this workshop included the
following.</p>
<ol>
<li>
<p align="justify">
Kelly
Kim Open Net Korea,</p>
</li><li>
<p align="justify">
Jyoti
Panday, CIS India,</p>
</li><li>
<p align="justify">
Gabrielle
Guillemin, Article 19,</p>
</li><li>
<p align="justify">
Rebecca
McKinnon on behalf of UNESCO</p>
</li><li>
<p align="justify">
Giancarlo
Frosio, Center for Internet and Society, Stanford Law School</p>
</li><li>
<p align="justify">
Nicolo
Zingales, Tilburg University</p>
</li><li>
<p align="justify">
Will
Hudson, Google</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals</a></p>
<p align="justify">
Transcript
of the workshop is available here
<a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2423-2015-11-13-ws-242-the-manila-principles-on-intermediary-liability-workshop-room-9">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2423-2015-11-13-ws-242-the-manila-principles-on-intermediary-liability-workshop-room-9</a></p>
<p align="justify">
Video link available here <a href="https://www.youtube.com/watch?v=kFLmzxXodjs">https://www.youtube.com/watch?v=kFLmzxXodjs</a></p>
<p align="justify"> <strong>ACCESSIBILITY</strong></p>
<p align="justify">
X.
Dynamic
Coalition
on Accessibility and Disability and Global Initiative for Inclusive
ICTs organised a workshop on <em><strong>Empowering
the Next Billion by Improving Accessibility</strong></em><em>
</em>at
Workshop Room 6 from 9:00 am to 10:30 am on 13 November, 2015. The
discussion focused on
the need and ways to remove accessibility barriers which prevent over
one billion potential users to benefit from the Internet, including
for essential services. Sunil
Abraham specifically spoke about the lack of compliance of existing
ICT infrastructure with well established accessibility standards
specifically relating to accessibility barriers in the disaster
management process. He discussed the barriers faced by persons with
physical or psychosocial disabilities. The
panelists for this discussion were the following.</p>
<ol>
<li>
<p align="justify">
Francesca
Cesa Bianchi, G3ICT</p>
</li><li>
<p align="justify">
Cid
Torquato, Government of Brazil</p>
</li><li>
<p align="justify">
Carlos
Lauria, Microsoft Brazil</p>
</li><li>
<p align="justify">
Sunil
Abraham, CIS India</p>
</li><li>
<p align="justify">
Derrick
L. Cogburn, Institute on Disability and Public Policy (IDPP) for the
ASEAN(Association of Southeast Asian Nations) Region</p>
</li><li>
<p align="justify">
Fernando
H. F. Botelho, F123 Consulting</p>
</li><li>
<p align="justify">
Gunela
Astbrink, GSA InfoComm</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<u><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals</a></u></p>
<p align="justify">
Transcript
of the workshop is available here
<u><a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2438-2015-11-13-ws-253-empowering-the-next-billion-by-improving-accessibility-workshop-room-3">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2438-2015-11-13-ws-253-empowering-the-next-billion-by-improving-accessibility-workshop-room-3</a></u></p>
<p align="justify">
Video
Link Empowering
the next billion by improving accessibility <a href="https://www.youtube.com/watch?v=7RZlWvJAXxs">https://www.youtube.com/watch?v=7RZlWvJAXxs</a></p>
<p align="justify"> <strong>OPENNESS</strong></p>
<p align="justify">
XI.
A
workshop on <em><strong>FOSS
& a Free, Open Internet: Synergies for Development</strong></em>
was organized at Workshop Room 7 from 2:00 pm to 3:30 pm on 13
November, 2015. The discussion was focused on the increasing risk to
openness of the internet and the ability of present & future
generations to use technology to improve their lives. The panel shred
different perspectives about the future co-development
of FOSS and a free, open Internet; the threats that are emerging; and
ways for communities to surmount these. Sunil
Abraham emphasised the importance of free software, open standards,
open access and access to knowledge and the lack of this mandate in
the draft outcome document for upcoming WSIS+10 review and called for
inclusion of the same. Pranesh Prakash further contributed to the
discussion by emphasizing the need for free open source software with
end‑to‑end encryption and traffic level encryption based
on open standards which are decentralized and work through federated
networks. The
panellists for this discussion were the following.</p>
<ol>
<li>
<p align="justify">
Satish
Babu, Technical Community, Chair, ISOC-TRV, Kerala, India</p>
</li><li>
<p align="justify">
Judy
Okite, Civil Society, FOSS Foundation for Africa</p>
</li><li>
<p align="justify">
Mishi
Choudhary, Private Sector, Software Freedom Law Centre, New York</p>
</li><li>
<p align="justify">
Fernando
Botelho, Private Sector, heads F123 Systems, Brazil</p>
</li><li>
<p align="justify">
Sunil
Abraham, CIS
India</p>
</li><li>
<p align="justify">
Pranesh
Prakash, CIS
India</p>
</li><li>
<p align="justify">
Nnenna
Nwakanma- WWW.Foundation</p>
</li><li>
<p align="justify">
Yves
MIEZAN EZO, Open Source strategy consultant</p>
</li><li>
<p align="justify">
Corinto
Meffe, Advisor to the President and Directors, SERPRO, Brazil</p>
</li><li>
<p align="justify">
Frank
Coelho de Alcantara, Professor, Universidade Positivo, Brazil</p>
</li><li>
<p align="justify">
Caroline
Burle, Institutional and International Relations, W3C Brazil Office
and Center of Studies on Web Technologies</p>
</li></ol>
<p align="justify">
Detailed
description of the workshop is available here
<u><a href="http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals" target="_top">http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals</a></u></p>
<p align="justify">
Transcript
of the workshop is available here
<u><a href="http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2468-2015-11-13-ws10-foss-and-a-free-open-internet-synergies-for-development-workshop-room-7" target="_top">http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2468-2015-11-13-ws10-foss-and-a-free-open-internet-synergies-for-development-workshop-room-7</a></u></p>
<p align="justify">
Video
link available here <a href="https://www.youtube.com/watch?v=lwUq0LTLnDs">https://www.youtube.com/watch?v=lwUq0LTLnDs</a></p>
<p align="justify">
<br /><br /></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/summary-report-internet-governance-forum-2015'>http://editors.cis-india.org/internet-governance/blog/summary-report-internet-governance-forum-2015</a>
</p>
No publisherjyotiAccess to KnowledgeBig DataFreedom of Speech and ExpressionEncryptionInternet Governance ForumIntermediary LiabilityAccountabilityInternet GovernanceCensorshipCyber SecurityDigital GovernanceAnonymityCivil SocietyBlocking2015-11-30T10:47:13ZBlog EntryPredictive Policing: What is it, How it works, and its Legal Implications
http://editors.cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications
<b>This article reviews literature surrounding big data and predictive policing and provides an analysis of the legal implications of using predictive policing techniques in the Indian context.</b>
<h2 style="text-align: justify; ">Introduction</h2>
<p style="text-align: justify; ">For the longest time, humans have been obsessed with prediction. Perhaps the most well-known oracle in history, Pythia, the infallible Oracle of Delphi was said to predict future events in hysterical outbursts on the seventh day of the month, inspired by the god Apollo himself. This fascination with informing ourselves about future events has hardly subsided in us humans. What has changed however is the methods we employ to do so. The development of Big data technologies for one, has seen radical applications into many parts of life as we know it, including enhancing our ability to make accurate predictions about the future.</p>
<p style="text-align: justify; ">One notable application of Big data into prediction caters to another basic need since the dawn of human civilisation, the need to protect our communities and cities. The word 'police' itself originates from the Greek word '<i>polis'</i>, which means city. The melding of these two concepts prediction and policing has come together in the practice of Predictive policing, which is the application of computer modelling to historical crime data and metadata to predict future criminal activity<a href="#_ftn1" name="_ftnref1">[1]</a><b>. </b>In the subsequent sections, I will attempt an introduction of predictive policing and explain some of the main methods within the domain of predictive policing. Because of the disruptive nature of these technologies, it will also be prudent to expand on the implications predictive technologies have for justice, privacy protections and protections against discrimination among others.</p>
<p style="text-align: justify; ">In introducing the concept of predictive policing, my first step is to give a short explanation about current predictive analytics techniques, because these techniques are the ones which are applied into a law enforcement context as predictive policing.</p>
<h2 style="text-align: justify; ">What is predictive analysis</h2>
<p style="text-align: justify; ">Facilitated by the availability of big data, predictive analytics uses algorithms to recognise data patterns and predict future outcomes<a href="#_ftn2" name="_ftnref2">[2]</a>. Predictive analytics encompasses data mining, predictive modeling, machine learning, and forecasting<a href="#_ftn3" name="_ftnref3">[3]</a>. Predictive analytics also relies heavily on machine learning and artificial intelligence approaches <a href="#_ftn4" name="_ftnref4">[4]</a>. The aim of such analysis is to identify relationships among variables that may not be immediately apparent using hypothesis-driven methods.<a href="#_ftn5" name="_ftnref5">[5]</a> In the mainstream media, one of the most infamous stories about the use of predictive analysis comes from USA, regarding a department store Target and their data analytics practices <a href="#_ftn6" name="_ftnref6">[6]</a>. Target mined data from purchasing patterns of people who signed onto their baby registry. From this they were able to predict approximately when customers may be due and target advertisements accordingly. In the noted story, they were so successful that they predicted pregnancy before the pregnant girl's father knew she was pregnant. <a href="#_ftn7" name="_ftnref7">[7]</a></p>
<h3 style="text-align: justify; ">Examples of predictive analytics</h3>
<ul>
<li>Predicting the success of a movie based on its online ratings<a href="#_ftn8" name="_ftnref8">[8]</a></li>
<li>Many universities, sometimes in partnership with other firms use predictive analytics to provide course recommendations to students, track student performance, personalize curriculum to individual students and foster networking between students.<a href="#_ftn9" name="_ftnref9">[9]</a></li>
<li>Predictive Analysis of Corporate Bond Indices Returns<a href="#_ftn10" name="_ftnref10">[10]</a></li>
</ul>
<h2 style="text-align: justify; ">Relationship between predictive analytics and predictive policing</h2>
<p style="text-align: justify; ">The same techniques used in many of the predictive methods mentioned above find application into some predictive policing methods. However two important points need to be raised:</p>
<p style="text-align: justify; ">First, predictive analytics is actually a subset of predictive policing. This is because while the steps in creating a predictive model, of defining a target variable, exposing your model to training data, selecting appropriate features and finally running predictive analysis <a href="#_ftn11" name="_ftnref11">[11]</a> maybe the same in a policing context, there are other methods which may be used to predict crime, but which do not rely on data mining. These techniques may instead use other methods, such as some of those detailed below along with data about historical crime to generate predictions.</p>
<p style="text-align: justify; ">In her article "Policing by Numbers: Big Data and the Fourth Amendment"<a href="#_ftn12" name="_ftnref12">[12]</a>, Joh categorises 3 main applications of Big data into policing. These are Predictive Policing, Domain Awareness systems and Genetic Data Banks. Genetic data banks refer to maintaining large databases of DNA that was collected as part of the justice system. Issues arise when the DNA collected is repurposed in order to conduct familial searches, instead of being used for corroborating identity. Familial searches may have disproportionate impacts on minority races. Domain Awareness systems use various computer software and other digital surveillance tools such as Geographical Information Systems <a href="#_ftn13" name="_ftnref13">[13]</a> or more illicit ones such as Black Rooms<a href="#_ftn14" name="_ftnref14">[14]</a> to "help police create a software-enhanced picture of the present, using thousands of data points from multiple sources within a city" <a href="#_ftn15" name="_ftnref15">[15]</a>. I believe Joh was very accurate in separating Predictive Policing from Domain Awareness systems, especially when it comes to analysing the implications of the various applications of Big data into policing.</p>
<p style="text-align: justify; ">In such an analysis of the implications of using predictive policing methods, the issues surrounding predictive technologies often get conflated with larger issues about the application of big data into law enforcement. That opens the debate up to questions about overly intrusive evidence gathering and mass surveillance systems, which though used along with predictive technology, are not themselves predictive in nature. In this article, I aim to concentrate on the specific implications that arise due to predictive methods.</p>
<p style="text-align: justify; ">One important point regarding the impact of predictive policing is how the insights that predictive policing methods offer are used. There is much support for the idea that predictive policing does not replace policing methods, but actually augments them. The RAND report specifically cites one myth about predictive policing as "the computer will do everything for you<a href="#_ftn16" name="_ftnref16">[16]</a>". In reality police officers need to act on the recommendations provided by the technologies.</p>
<h2 style="text-align: justify; ">What is Predictive policing?</h2>
<p style="text-align: justify; ">Predictive policing is the "application of analytical techniques-particularly quantitative techniques-to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions".<a href="#_ftn17" name="_ftnref17">[17]</a> It is important to note that the use of data and statistics to inform policing is not new. Indeed, even twenty years ago, before the deluge of big data we have today, law enforcement regimes such as the New York Police Department (NYPD) were already using crime data in a major way. In order to keep track of crime trends, NYPD used the software CompStat<a href="#_ftn18" name="_ftnref18">[18]</a> to map "crime statistics along with other indicators of problems, such as the locations of crime victims and gun arrests"<a href="#_ftn19" name="_ftnref19">[19]</a>. The senior officers used the information provided by CompStat to monitor trends of crimes on a daily basis and such monitoring became an instrumental way to track the performance of police agencies<a href="#_ftn20" name="_ftnref20">[20]</a>. CompStat has since seen application in many other jurisdictions <a href="#_ftn21" name="_ftnref21">[21]</a>.</p>
<p style="text-align: justify; ">But what is new is the amount of data available for collection, as well as the ease with which organisations can analyse and draw insightful results from that data. Specifically, new technologies allow for far more rigorous interrogation of data and wide-ranging applications, including adding greater accuracy to the prediction of future incidence of crime.</p>
<h2 style="text-align: justify; ">Predictive Policing methods</h2>
<p style="text-align: justify; ">Some methods of predictive policing involve application of known standard statistical methods, while other methods involve modifying these standard techniques. Predictive techniques that forecast future criminal activities can be framed around six analytic categories. They all may overlap in the sense that multiple techniques are used to create actual predictive policing software and in fact it is similar theories of criminology which undergird many of these methods, but the categorisation in such a way helps clarify the concept of predictive policing. The basis for the categorisation below comes from a RAND Corporation report entitled 'Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations' <a href="#_ftn22" name="_ftnref22">[22]</a>, which is a comprehensive and detailed contribution to scholarship in this nascent area.</p>
<p style="text-align: justify; ">Hot spot analysis: Methods involving hot spot analysis attempt to "predict areas of increased crime risk based on historical crime data"<a href="#_ftn23" name="_ftnref23">[23]</a>. The premise behind such methods lies in the adage that "crime tends to be lumpy" <a href="#_ftn24" name="_ftnref24">[24]</a>. Hot Spot analysis seeks to map out these previous incidences of crime in order to inform potential future crime.</p>
<p style="text-align: justify; ">Regression methods: A regression aims to find relationships between independent variables (factors that may influence criminal activity) and certain variables that one aims to predict. Hence, this method would track more variables than just crime history.</p>
<p style="text-align: justify; ">Data mining techniques: Data mining attempts to recognise patterns in data and use it to make predictions about the future. One important variant in the various types of data mining methods used in policing are different types of algorithms that are used to mine data in different ways. These are dependent on the nature of the data the predictive model was trained on and will be used to interrogate in the future. Two broad categories of algorithms commonly used are clustering algorithms and classification algorithms:</p>
<p style="text-align: justify; ">· Clustering algorithms "form a class of data mining approaches that seek to group data into clusters with similar attributes" <a href="#_ftn25" name="_ftnref25">[25]</a>. One example of clustering algorithms is spatial clustering algorithms, which use geospatial crime incident data to predict future hot spots for crime<a href="#_ftn26" name="_ftnref26">[26]</a>.</p>
<p style="text-align: justify; ">· Classification algorithms "seek to establish rules assigning a class or label to events"<a href="#_ftn27" name="_ftnref27">[27]</a>. These algorithms use training data sets "to learn the patterns that determine the class of an observation"<a href="#_ftn28" name="_ftnref28">[28]</a> The patterns identified by the algorithm will be applied to future data, and where applicable, the algorithm will recognise similar patterns in the data. This can be used to make predictions about future criminal activity for example.</p>
<p style="text-align: justify; ">Near-repeat methods: Near-repeat methods work off the assumption that future crimes will take place close to timing and location of current crimes. Hence, it could be postulated that areas of high crime will experience more crime in the near future<a href="#_ftn29" name="_ftnref29">[29]</a>. This involves the use of a 'self-exciting' algorithm, very similar to algorithms modelling earthquake aftershocks <a href="#_ftn30" name="_ftnref30">[30]</a>. The premise undergirding such methods is very similar to that of hot spot analysis.</p>
<p style="text-align: justify; ">Spatiotemporal analysis<b>: </b>Using "environmental and temporal features of the crime location" <a href="#_ftn31" name="_ftnref31">[31]</a> as the basis for predicting future crime. By combining the spatiotemporal features of the crime area with crime incident data, police could use the resultant information to predict the location and time of future crimes. Examples of factors that may be considered include timing of crimes, weather, distance from highways, time from payday and many more.</p>
<p style="text-align: justify; ">Risk terrain analysis: Analyses other factors that are useful in predicting crimes. Examples of such factors include "the social, physical, and behavioural factors that make certain areas more likely to be affected by crime"<a href="#_ftn32" name="_ftnref32">[32]</a></p>
<p style="text-align: justify; ">Various methods listed above are used, often together, to predict the where and when a crime may take place or even potential victims. The unifying thread which relates these methods is their dependence on historical crime data.</p>
<h2 style="text-align: justify; ">Examples of predictive policing:</h2>
<p style="text-align: justify; ">Most uses of predictive policing that have been studied and reviewed in scholarly work come from the USA, though I will detail one case study from Derbyshire, UK. Below is a collation of various methods that are a practical application of the methods raised above.</p>
<p style="text-align: justify; ">Hot Spot analysis in Sacramento: In February 2011, Sacramento Police Department began using hot spot analysis along with research on optimal patrol time to act as a sufficient deterrent to inform how they patrol high-risk areas. This policy was aimed at preventing serious crimes by patrolling these predicted hot spots. In places where there was such patrolling, serious crimes reduced by a quarter with no significant increases such crimes in surrounding areas<a href="#_ftn33" name="_ftnref33">[33]</a>.</p>
<p style="text-align: justify; ">Data Mining and Hot Spot Mapping in Derbyshire, UK: The Safer Derbyshire Partnership, a group of law enforcement agencies and municipal authorities sought to identify juvenile crime hotspots<a href="#_ftn34" name="_ftnref34">[34]</a>. They used MapInfo software to combine "multiple discrete data sets to create detailed maps and visualisations of criminal activity, including temporal and spatial hotspots" <a href="#_ftn35" name="_ftnref35">[35]</a>. This information informed law enforcement about how to optimally deploy their resources.</p>
<p style="text-align: justify; ">Regression models in Pittsburgh: Researchers used reports from Pittsburgh Bureau of Police about violent crimes and "leading indicator" <a href="#_ftn36" name="_ftnref36">[36]</a> crimes, crimes that were relatively minor but which could be a sign of potential future violent offences. The researcher ran analysis of areas with violent crimes, which were used as the dependent variable in analysing whether violent crimes in certain areas could be predicted by the leading indicator data. From the 93 significant violent crime areas that were studied, 19 areas were successfully predicted by the leading indicator data.<a href="#_ftn37" name="_ftnref37">[37]</a></p>
<p style="text-align: justify; ">Risk terrain modelling analysis in Morris County, New Jersey: Police in Morris County, used risk terrain analysis to tackle violent crimes and burglaries. They considered five inputs in their model: "past burglaries, the address of individuals recently arrested for property crimes, proximity to major highways, the geographic concentration of young men and the location of apartment complexes and hotels." <a href="#_ftn38" name="_ftnref38">[38]</a> The Morris County law enforcement officials linked the significant reductions in violent and property crime to their use of risk terrain modelling<a href="#_ftn39" name="_ftnref39">[39]</a>.</p>
<p style="text-align: justify; ">Near-repeat & hot spot analysis used by Santa Cruz Police Department: Uses PredPol software that applies the Mohler's algorithm <a href="#_ftn40" name="_ftnref40">[40]</a> to a database with five years' worth of crime data to assess the likelihood of future crime occurring in the geographic areas within the city. Before going on shift, officers receive information identifying 15 such areas with the highest probability of crime<a href="#_ftn41" name="_ftnref41">[41]</a>. The initiative has been cited as being very successful at reducing burglaries, and was used in Los Angeles and Richmond, Virginia<a href="#_ftn42" name="_ftnref42">[42]</a>.</p>
<p style="text-align: justify; ">Data Mining and Spatiotemporal analysis to predict future criminal activities in Chicago: Officers in Chicago Police Department made visits to people their software predicted were likely to be involved in violent crimes<a href="#_ftn43" name="_ftnref43">[43]</a>, guided by an algorithm-generated "Heat List"<a href="#_ftn44" name="_ftnref44">[44]</a>. Some of the inputs used in the predictions include some types of arrest records, gun ownership, social networks<a href="#_ftn45" name="_ftnref45">[45]</a> (police analysis of social networking is also a rising trend in predictive policing<a href="#_ftn46" name="_ftnref46">[46]</a>) and generally type of people you are acquainted with <a href="#_ftn47" name="_ftnref47">[47]</a> among others, but the full list of the factors are not public. The list sends police officers (or sometimes mails letters) to peoples' homes to offer social services or deliver warnings about the consequences for offending. Based in part on the information provided by the algorithm, officers may provide people on the Heat List information about vocational training programs or warnings about how Federal Law provides harsher punishments for reoffending<a href="#_ftn48" name="_ftnref48">[48]</a>.</p>
<h2 style="text-align: justify; ">Predictive policing in India</h2>
<p style="text-align: justify; ">In this section, I map out some of the developments in the field of predictive policing within India. On the whole, predictive policing is still very new in India, with Jharkhand being the only state that appears to already have concrete plans in place to introduce predictive policing.</p>
<h3 style="text-align: justify; ">Jharkhand Police</h3>
<p style="text-align: justify; ">The Jharkhand police began developing their IT infrastructure such as a Geographic Information System (GIS) and Server room when they received funding for Rs. 18.5 crore from the Ministry of Home Affairs<a href="#_ftn49" name="_ftnref49">[49]</a>. The Open Group on E-governance (OGE), founded as a collaboration between the Jharkhand Police and National Informatics Centre<a href="#_ftn50" name="_ftnref50">[50]</a>, is now a multi-disciplinary group which takes on different projects related to IT<a href="#_ftn51" name="_ftnref51">[51]</a>. With regards to predictive policing, some members of OGE began development in 2013 of data mining software which will scan online records that are digitised. The emerging crime trends "can be a building block in the predictive policing project that the state police want to try."<a href="#_ftn52" name="_ftnref52">[52]</a></p>
<p style="text-align: justify; ">The Jharkhand Police was also reported in 2012 to be in the final stages of forming a partnership with IIM-Ranchi<a href="#_ftn53" name="_ftnref53">[53]</a>. It was alleged the Jharkhand police aimed to tap into IIM's advanced business analytics skills <a href="#_ftn54" name="_ftnref54">[54]</a>, skills that can be very useful in a predictive policing context. Mr Pradhan suggested that "predictive policing was based on intelligence-based patrol and rapid response"<a href="#_ftn55" name="_ftnref55">[55]</a> and that it could go a long way to dealing with the threat of Naxalism in Jharkhand<a href="#_ftn56" name="_ftnref56">[56]</a>.</p>
<p style="text-align: justify; ">However, in Jharkhand, the emphasis appears to be targeted at developing a massive Domain Awareness system, collecting data and creating new ways to present that data to officers on the ground, instead of architecting and using predictive policing software. For example, the Jharkhand police now have in place "a Naxal Information System, Crime Criminal Information System (to be integrated with the CCTNS) and a GIS that supplies customised maps that are vital to operations against Maoist groups"<a href="#_ftn57" name="_ftnref57">[57]</a>. The Jharkhand police's "Crime Analytics Dashboard" <a href="#_ftn58" name="_ftnref58">[58]</a> shows the incidence of crime according to type, location and presents it in an accessible portal, providing up-to-date information and undoubtedly raises the situational awareness of the officers. Arguably, the domain awareness systems that are taking shape in Jharkhand would pave the way for predictive policing methods to be applied in the future. These systems and hot spot maps seem to be the start of a new age of policing in Jharkhand.</p>
<h3 style="text-align: justify; ">Predictive Policing Research</h3>
<p style="text-align: justify; ">One promising idea for predictive policing in India comes from the research conducted by Lavanya Gupta and others entitled "Predicting Crime Rates for Predictive Policing"<a href="#_ftn59" name="_ftnref59">[59]</a>, which was a submission for the Gandhian Young Technological Innovation Award. The research uses regression modelling to predict future crime rates. Drawing from First Information Reports (FIRs) of violent crimes (murder, rape, kidnapping etc.) from Chandigarh Police, the team attempted "to extrapolate annual crime rate trends developed through time series models. This approach also involves correlating past crime trends with factors that will influence the future scope of crime, in particular demographic and macro-economic variables" <a href="#_ftn60" name="_ftnref60">[60]</a>. The researchers used early crime data as the training data for their model, which after some testing, eventually turned out to have an accuracy of around 88.2%.<a href="#_ftn61" name="_ftnref61">[61]</a> On the face of it, ideas like this could be the starting point for the introduction of predictive policing into India.</p>
<p style="text-align: justify; ">The rest of India's law enforcement bodies do not appear to be lagging behind. In the 44<sup>th</sup> All India police science congress, held in Gandhinagar, Gujarat in March this year, one of the Themes for discussion was the "Role of Preventive Forensics and latest developments in Voice Identification, Tele-forensics and Cyber Forensics"<a href="#_ftn62" name="_ftnref62">[62]</a>.Mr A K Singh, (Additional Director General of Police, Administration) the chairman of the event also said in an interview that there was to be a round-table DGs (Director General of Police) held at the conference to discuss predictive policing<a href="#_ftn63" name="_ftnref63">[63]</a>. Perhaps predictive policing in India may not be that far away from reality.</p>
<h3 style="text-align: justify; ">CCTNS and the building blocks of Predictive policing</h3>
<p style="text-align: justify; ">The Ministry of Home Affairs conceived of a Crime and Criminals Tracking and Network System (CCTNS) as part of national e-Governance plans. According to the website of the National Crime Records Bureau (NCRB), CCTNS aims to develop "a nationwide networked infrastructure for evolution of IT-enabled state-of-the-art tracking system around 'investigation of crime and detection of criminals' in real time" <a href="#_ftn64" name="_ftnref64">[64]</a></p>
<p style="text-align: justify; ">The plans for predictive policing seem in the works, but first steps that are needed in India across police forces involve digitizing data collection by the police, as well as connecting law enforcement agencies. The NCRB's website described the current possibility of exchange of information between neighbouring police stations, districts or states as being "next to impossible"<a href="#_ftn65" name="_ftnref65">[65]</a>. The aim of CCTNS is precisely to address this gap and integrate and connect the segregated law enforcement arms of the state in India, which would be a foundational step in any initiatives to apply predictive methods.</p>
<h2 style="text-align: justify; ">What are the implications of using predictive policing? Lessons from USA</h2>
<p style="text-align: justify; ">Despite the moves by law enforcement agencies to adopt predictive policing, one reality is that the implications of predictive policing methods are far from clear. This section will examine these implications on the carriage of justice and its use in law, as well as how it impacts privacy concerns for the individual. It frames the existing debates surrounding these issues with predictive policing, and aims to apply these principles into an Indian context.</p>
<h3 style="text-align: justify; ">Justice, Privacy & IV Amendment</h3>
<p style="text-align: justify; ">Two key concerns about how predictive policing methods may be used by law enforcement relate to how insights from predictive policing methods are acted upon and how courts interpret them. In the USA, this issue may finds its place under the scope of IV Amendment jurisprudence. The IV amendment states that all citizens are "secure from unreasonable searches and seizures of property by the government"<a href="#_ftn66" name="_ftnref66">[66]</a>. In this sense, the IV amendment forms the basis for search and surveillance law in the USA.</p>
<p style="text-align: justify; ">A central aspect of the IV Amendment jurisprudence is drawn from <i>United States v. Katz</i>. In <i>Katz</i>, the FBI attached a microphone to the outside of a public phone booth to record the conversations of Charles Katz, who was making phone calls related to illegal gambling. The court ruled that such actions constituted a search within the auspices of the 4<sup>th</sup> amendment. The ruling affirmed constitutional protection of all areas where someone has a "reasonable expectation of privacy"<a href="#_ftn67" name="_ftnref67">[67]</a>.</p>
<p style="text-align: justify; ">Later cases have provided useful tests for situations where government surveillance tactics may or may not be lawful, depending on whether it violates one's reasonable expectation of privacy. For example, in <i>United States v. Knotts</i>, the court held that "police use of an electronic beeper to follow a suspect surreptitiously did not constitute a Fourth Amendment search"<a href="#_ftn68" name="_ftnref68">[68]</a>. In fact, some argue that that the Supreme Court's reasoning in such cases suggests " any 'scientific enhancement' of the senses used by the police to watch activity falls outside of the Fourth Amendment's protections if the activity takes place in public"<a href="#_ftn69" name="_ftnref69">[69]</a>. This reasoning is based on the third party doctrine which holds that "if you voluntarily provide information to a third party, the IV Amendment does not preclude the government from accessing it without a warrant"<a href="#_ftn70" name="_ftnref70">[70]</a>. The clearest exposition of this reasoning was in Smith v. Maryland, where the presiding judges noted that "this Court consistently has held that a person has no legitimate expectation of privacy in information he voluntarily turns over to third parties"<a href="#_ftn71" name="_ftnref71">[71]</a>.</p>
<p style="text-align: justify; ">However, the third party has seen some challenge in recent time. In <i>United States v. Jones</i>, it was ruled that the government's warrantless GPS tracking of his vehicle 24 hours a day for 28 days violated his Fourth Amendment rights<a href="#_ftn72" name="_ftnref72">[72]</a>. Though the majority ruling was that warrantless GPS tracking constituted a search, it was in a concurring opinion written by Justice Sonya Sotomayor that such intrusive warrantless surveillance was said to infringe one's reasonable expectation of privacy. As Newell reflected on Sotomayor's opinion,</p>
<p style="text-align: justify; ">"Justice Sotomayor stated that the time had come for Fourth Amendment jurisprudence to discard the premise that legitimate expectations of privacy could only be found in situations of near or complete secrecy. Sotomayor argued that people should be able to maintain reasonable expectations of privacy in some information voluntarily disclosed to third parties"<a href="#_ftn73" name="_ftnref73">[73]</a>.</p>
<p style="text-align: justify; ">She said that the court's current reasoning on what constitutes reasonable expectations of privacy in information disclosed to third parties, such as email or phone records or even purchase histories, is "ill-suited to the digital age, in which people reveal a great deal of information about themselves to third parties in the course of carrying out mundane tasks"<a href="#_ftn74" name="_ftnref74">[74]</a>.</p>
<h3 style="text-align: justify; ">Predictive policing vs. Mass surveillance and Domain Awareness Systems</h3>
<p style="text-align: justify; ">However, there is an important distinction to be drawn between these cases and evidence from predictive policing. This has to do with the difference in nature of the evidence collection. Arguably, from Jones and others, what we see is that use of mass surveillance and domain awareness systems, drawing from Joh's categorisation of domain awareness systems as being distinct from predictive policing mentioned above, could potentially encroach on one's reasonable expectation of privacy. However, I think that predictive policing, and the possible implications for justice associated with it, its predictive harms, are quite distinct from what has been heard by courts thus far.</p>
<p style="text-align: justify; ">The reason for distinct risks between predictive harms and privacy harms originating from information gathering is related to the nature of predictive policing technologies, and how they are used. It is highly unlikely that the evidence submitted by the State to indict an offender will be mainly predictive in nature. For example, would it be possible to convict an accused person solely on the premise that he was predicted to be highly likely to commit a crime, and that subsequently he did? The legal standard of proving guilt beyond a reasonable doubt <a href="#_ftn75" name="_ftnref75">[75]</a> can hardly be met solely on predictive evidence for a multitude of reasons. Predictive policing methods could at most, be said to inform police about the risk of someone committing a crime or of crime happening at a certain location, as demonstrated above.</p>
<h4 style="text-align: justify; ">Predictive policing and Criminal Procedure</h4>
<p style="text-align: justify; ">It may therefore pay to analyse how predictive policing may be used across the various processes within the criminal justice system. In fact, in an analysis of the various stages of criminal procedure, from opening an investigation to gathering evidence, followed by arrest, trial, conviction and sentencing, we see that as the individual gets subject to more serious incursions or sanctions by the state, it takes a higher standard of certainty about wrongdoing and a higher burden of proof, in order to legitimize that particular action.</p>
<p style="text-align: justify; ">Hence, at more advanced stages of the criminal justice process such as seeking arrest warrants or trial, it is very unlikely that predictive policing on its own can have a tangible impact, because the nature of predictive evidence is probability based. It aims to calculate the risk of future crime occurring based on statistical analysis of past crime data<a href="#_ftn76" name="_ftnref76">[76]</a>. While extremely useful, probabilities on their own will not come remotely close meet the legal standards of proving 'guilt beyond reasonable doubt'. It may be at the earlier stages of the criminal justice process that evidence predictive policing might see more widespread application, in terms of applying for search warrants and searching suspicious people while on patrol.</p>
<p style="text-align: justify; ">In fact, in the law enforcement context, prediction as a concept is not new to justice. Both courts and law enforcement officials already make predictions about future likelihood of crimes. In the case of issuing warrants, the IV amendment makes provisions that law enforcement officials show that the potential search is based "upon probable cause"<a href="#_ftn77" name="_ftnref77">[77]</a> in order for a judge to grant a warrant. In <i>US v. Brinegar</i>, probable cause was defined as existing "where the facts and circumstances within the officers' knowledge, and of which they have reasonably trustworthy information, are sufficient in themselves to warrant a belief by a man of reasonable caution that a crime is being committed" <a href="#_ftn78" name="_ftnref78">[78]</a>. Again, this legal standard seems too high for predictive evidence meet.</p>
<p style="text-align: justify; ">However, the police also have an important role to play in preventing crimes by looking out for potential crimes while on patrol or while doing surveillance. When the police stop a civilian on the road to search him, reasonable suspicion must be established. This standard of reasonable suspicion was defined in most clearly in <i>Terry v. Ohio</i>, which required police to "be able to point to specific and articulable facts which, taken together with rational inferences from those facts, reasonably warrant that intrusion"<a href="#_ftn79" name="_ftnref79">[79]</a>. Therefore, "reasonable suspicion that 'criminal activity may be afoot' is at base a prediction that the facts and circumstances warrant the reasonable prediction that a crime is occurring or will occur"<a href="#_ftn80" name="_ftnref80">[80]</a>. Despite the assertion that "there are as of yet no reported cases on predictive policing in the Fourth Amendment context"<a href="#_ftn81" name="_ftnref81">[81]</a>, examining the impact of predictive policing on the doctrine of reasonable suspicion could be very instructive in understanding the implications for justice and privacy <a href="#_ftn82" name="_ftnref82">[82]</a>.</p>
<h3 style="text-align: justify; ">Predictive Policing and Reasonable Suspicion</h3>
<p style="text-align: justify; ">Ferguson's insightful contribution to this area of scholarship involves the identification of existing areas where prediction already takes place in policing, and analogising them into a predictive policing context<a href="#_ftn83" name="_ftnref83">[83]</a>. These three areas are: responding to tips, profiling, and high crime areas (hot spots).</p>
<h4 style="text-align: justify; ">Tips</h4>
<p style="text-align: justify; ">Tips are pieces of information shared with the police by members of the public. Often tips, either anonymous or from known police informants, may predict future actions of certain people, and require the police to act on this information. The precedent for understanding the role of tips in probable cause comes from <i>Illinois v. Gates</i><a href="#_ftn84" name="_ftnref84">[84]</a>. It was held that "an informant's 'veracity,' 'reliability,' and 'basis of knowledge'-remain 'highly relevant in determining the value'"<a href="#_ftn85" name="_ftnref85">[85]</a> of the said tip. Anonymous tips need to be detailed, timely and individualised enough<a href="#_ftn86" name="_ftnref86">[86]</a> to justify reasonable suspicion <a href="#_ftn87" name="_ftnref87">[87]</a>. And when the informant is known to be reliable, then his prior reliability may justify reasonable suspicion despite lacking a basis in knowledge<a href="#_ftn88" name="_ftnref88">[88]</a>.</p>
<p style="text-align: justify; ">Ferguson argues that whereas predictive policing cannot provide individualised tips, it is possible to consider reliable tips about certain areas as a parallel to predictive policing<a href="#_ftn89" name="_ftnref89">[89]</a>. And since the courts had shown a preference for reliability even in the face of a weak basis in knowledge, it is possible to see the reasonable suspicion standard change in its application<a href="#_ftn90" name="_ftnref90">[90]</a>. It also implies that IV protections may be different in places where crime is predicted to occur <a href="#_ftn91" name="_ftnref91">[91]</a>.</p>
<h4 style="text-align: justify; ">Profiling</h4>
<p style="text-align: justify; ">Despite the negative connotations and controversial overtones at the mere sound of the word, profiling is already a method commonly used by law enforcement. For example, after a crime has been committed and general features of the suspect identified by witnesses, police often stop civilians who fit this description. Another example of profiling is common in combating drug trafficking<a href="#_ftn92" name="_ftnref92">[92]</a>, where agents keep track of travellers at airports to watch for suspicious behaviour. Based on their experience of common traits which distinguish drug traffickers from regular travellers (a profile), agents may search travellers if they fit the profile<a href="#_ftn93" name="_ftnref93">[93]</a>. In the case of <i>United States v. Sokolow</i><a href="#_ftn94" name="_ftnref94">[94]</a>, the courts "recognized that a drug courier profile is not an irrelevant or inappropriate consideration that, taken in the totality of circumstances, can be considered in a reasonable suspicion determination" <a href="#_ftn95" name="_ftnref95">[95]</a>. Similar lines of thinking could be employed in observing people exchanging small amounts of money in an area known for high levels of drug activity, conceiving predictive actions as a form of profile<a href="#_ftn96" name="_ftnref96">[96]</a>.</p>
<p style="text-align: justify; ">It is valid to consider predictive policing as a form of profiling<a href="#_ftn97" name="_ftnref97">[97]</a>, but Ferguson argues that the predictive policing context means this 'new form' of profiling could change IV analysis. The premise behind such an argument lies in the fact that a prediction made by some algorithm about potential high risk of crime in a certain area, could be taken in conjunction observations of ordinarily innocuous events. Read in the totality of circumstances, these two threads may justify individual reasonable suspicion <a href="#_ftn98" name="_ftnref98">[98]</a>. For example, a man looking into cars at a parking lot may not by itself justify reasonable suspicion, but taken together with a prediction of high risk of car theft at that locality, it may well justify reasonable suspicion. It is this impact of predictive policing, which influences the analysis of reasonable suspicion in a totality of circumstances that may represent new implications for courts looking at IV amendment protections.</p>
<h5 style="text-align: justify; ">Profiling, Predictive Policing and Discrimination</h5>
<p style="text-align: justify; ">The above sections have already brought up the point that law enforcement agencies already utilize profiling methods in their operations. Also, as the sections on how predictive analytics works and on methods of predictive policing make clear, predictive policing definitely incorporates the development of profiles for predicting future criminal activity. Concerns about predictive models generate potentially discriminatory predictions therefore are very serious, and need addressing. Potential discrimination may be either overt, though far less likely, or unintended. A valuable case study of which sheds light on such discriminatory data mining practices can be found in US Labour law. It was shown how predictive models could be discriminatory at various stages, from conceptualising the model and training it with training data, to eventually selecting inappropriate features to search for <a href="#_ftn99" name="_ftnref99">[99]</a>. It is also possible for data scientists to (intentionally or not) use proxies for identifiers like race, income level, health condition and religion. Barocas and Selbst argue that "the current distribution of relevant attributes-attributes that can and should be taken into consideration in apportioning opportunities fairly-are demonstrably correlated with sensitive attributes" <a href="#_ftn100" name="_ftnref100">[100]</a>. Hence, what may result is unintended discrimination, as predictive models and their subjective and implicit biases are reflected in predicted decisions, or that the discrimination is not even accounted for in the first place. While I have not found any case law where courts have examined such situations in a criminal context, at the very least, law enforcement agencies need to be aware of these possibilities and guard against any forms of discriminatory profiling.</p>
<p style="text-align: justify; ">However, Ferguson argues that "the precision of the technology may in fact provide more protection for citizens in broadly defined high crime areas" <a href="#_ftn101" name="_ftnref101">[101]</a>. This is because the label of a 'high-crime area' may no longer apply to large areas but instead to very specific areas of criminal activity. This implies that previously defined areas of high crime, like entire neighbourhoods may not be scrutinised in such detail. Instead, police now may be more precise in locating and policing areas of high crime, such as an individual street corner or a particular block of flats instead of an entire locality.</p>
<h4 style="text-align: justify; ">Hot Spots</h4>
<p style="text-align: justify; ">Courts have also considered the existence of notoriously 'high-crime areas as part of considering reasonable suspicion<a href="#_ftn102" name="_ftnref102">[102]</a>. This was seen in <i>Illinois v. Wardlow</i> <a href="#_ftn103" name="_ftnref103">[103]</a>, where the "high crime nature of an area can be considered in evaluating the officer's objective suspicion"<a href="#_ftn104" name="_ftnref104">[104]</a>. Many cases have since applied this reasoning without scrutinising the predictive value of such a label. In fact, Ferguson asserts that such labelling has questionable evidential value<a href="#_ftn105" name="_ftnref105">[105]</a>. He uses the facts of the <i>Wardlow </i>case itself to challenge the 'high crime area' factor. Ferguson cites the reasoning of one of the judges in the case:</p>
<p style="text-align: justify; ">"While the area in question-Chicago's District 11-was a low-income area known for violent crimes, how that information factored into a predictive judgment about a man holding a bag in the afternoon is not immediately clear."<a href="#_ftn106" name="_ftnref106">[106]</a></p>
<p style="text-align: justify; ">Especially because "the most basic models of predictive policing rely on past crimes"<a href="#_ftn107" name="_ftnref107">[107]</a>, it is likely that the predictive policing methods like hot spot or spatiotemporal analysis and risk terrain modelling may help to gather or build data models about high crime areas. Furthermore, the mathematical rigour of the predictive modelling could help clarify the term 'high crime area'. As Ferguson argues, "courts may no longer need to rely on the generalized high crime area terminology when more particularized and more relevant information is available" <a href="#_ftn108" name="_ftnref108">[108]</a>.</p>
<h4 style="text-align: justify; ">Summary</h4>
<p style="text-align: justify; ">Ferguson synthesises four themes to which encapsulate reasonable suspicion analysis:</p>
<ol>
<li> Predictive information is not enough on its own. Instead, it is "considered relevant to the totality of circumstances, but must be corroborated by direct police observation"<a href="#_ftn109" name="_ftnref109">[109]</a>.</li>
<li>The prediction must also "be particularized to a person, a profile, or a place, in a way that directly connects the suspected crime to the suspected person, profile, or place"<a href="#_ftn110" name="_ftnref110">[110]</a>.</li>
<li>It must also be detailed enough to distinguish a person or place from others not the focus of the prediction <a href="#_ftn111" name="_ftnref111">[111]</a>.</li>
<li>Finally, predicted information becomes less valuable over time. Hence it must be acted on quickly or be lost <a href="#_ftn112" name="_ftnref112">[112]</a>.</li>
</ol>
<h4 style="text-align: justify; ">Conclusions from America</h4>
<p style="text-align: justify; ">The main conclusion to draw from the analysis of the parallels between existing predictions in IV amendment law and predictive policing is that "predictive policing will impact the reasonable suspicion calculus by becoming a factor within the totality of circumstances test"<a href="#_ftn113" name="_ftnref113">[113]</a>. Naturally, it reaffirms the imperative for predictive techniques to collect reliable data <a href="#_ftn114" name="_ftnref114">[114]</a> and analyse it transparently<a href="#_ftn115" name="_ftnref115">[115]</a>. Moreover, in order for courts to evaluate the reliability of the data and the processes used (since predictive methods become part of the reasonable suspicion calculus), courts need to be able to analyse the predictive process. This has implications for the how hearings may be conducted, for how legal adjudicators may require training and many more. Another important concern is that the model of predictive information and police corroboration or direct observation<a href="#_ftn116" name="_ftnref116">[116]</a> may mean that in areas which were predicted to have low risk of crime, the reasonable suspicion doctrine works against law enforcement. There may be less effort paid to patrolling these other areas as a result of predictions.</p>
<h2 style="text-align: justify; ">Implications for India</h2>
<p style="text-align: justify; ">While there have been no cases directly involving predictive policing methods, it would be prudent to examine the parts of Indian law which would inform the calculus on the lawfulness of using predictive policing methods. A useful lens to examine this might be found in the observation that prediction is not in itself a novel concept in justice, and is already used by courts and law enforcement in numerous circumstances.</p>
<h3 style="text-align: justify; ">Criminal Procedure in Non-Warrant Contexts</h3>
<p style="text-align: justify; ">The most logical way to begin analysing the legal implications of predictive policing in India may probably involve identifying parallels between American and Indian criminal procedure, specifically searching for instances where 'reasonable suspicion' or some analogous requirement exists for justifying police searches.</p>
<p style="text-align: justify; ">In non-warrant scenarios, we find conditions for officers to conduct such a warrantless search in Section 165 of the Criminal Procedure Code (Cr PC). For clarity purposes I have stated section 165 (1) in full:</p>
<p style="text-align: justify; ">"Whenever an officer in charge of a police station or a police officer making an investigation <b>has reasonable grounds</b> for believing that anything necessary for the purposes of an investigation into any offence which he is authorised to investigate may be found in any place with the limits of the police station of which he is in charge, or to which he is attached, and that such thing cannot in his opinion be otherwise obtained without undue delay, such officer may, after recording in writing the grounds of his belief and specifying in such writing, so far as possible, the thing for which search is to be made, search, or cause search to be made, for such thing in any place within the limits of such station." <a href="#_ftn117" name="_ftnref117">[117]</a></p>
<p style="text-align: justify; ">However, India differs from the USA in that its Cr PC allows for police to arrest individuals without a warrant as well. As observed in <i>Gulab Chand Upadhyaya vs State Of U.P</i>, "Section 41 Cr PC gives the power to the police to arrest without warrant in cognizable offences, in cases enumerated in that Section. One such case is of receipt of a 'reasonable complaint' or 'credible information' or 'reasonable suspicion'" <a href="#_ftn118" name="_ftnref118">[118]</a> Like above, I have stated section 41 (1) and subsection (a) in full:</p>
<p style="text-align: justify; ">"41. When police may arrest without warrant.</p>
<p style="text-align: justify; "><a href="http://indiankanoon.org/doc/507354/">(1)</a> Any police officer may without an order from a Magistrate and without a warrant, arrest any person-</p>
<p style="text-align: justify; "><a href="http://indiankanoon.org/doc/1315149/">(a)</a> who has been concerned in any cognizable offence, or against whom a <b>reasonable complaint has been made, or credible information has been received, or a reasonable suspicion exists</b>, of his having been so concerned"<a href="#_ftn119" name="_ftnref119">[119]</a></p>
<p style="text-align: justify; ">In analysing the above sections of Indian criminal procedure from a predictive policing angle, one may find both similarities and differences between the proposed American approach and possible Indian approaches to interpreting or incorporating predictive policing evidence.</p>
<h4 style="text-align: justify; ">Similarity of 'reasonable suspicion' requirement</h4>
<p style="text-align: justify; ">For one, the requirement for "reasonable grounds" or "reasonable suspicion" seems to be analogous to the American doctrine of reasonable suspicion. This suggests that the concepts used in forming reasonable suspicion, for the police to "be able to point to specific and articulable facts which, taken together with rational inferences from those facts, reasonably warrant that intrusion"<a href="#_ftn120" name="_ftnref120">[120]</a> may also be useful in the Indian context.</p>
<p style="text-align: justify; ">One case which sheds light on an Indian interpretation of reasonable suspicion or grounds is <i>State of Punjab v. Balbir Singh<a href="#_ftn121" name="_ftnref121"><b>[121]</b></a></i>. In that case, the court observes a requirement for "reason to believe that such an offence under Chapter IV has been committed and, therefore, an arrest or search was necessary as contemplated under these provisions"<a href="#_ftn122" name="_ftnref122">[122]</a> in the context of Section 41 and 42 in The Narcotic Drugs and Psychotropic Substances Act, 1985<a href="#_ftn123" name="_ftnref123">[123]</a>. In examining the requirement of having "reason to believe", the court draws on <i>Partap Singh (Dr)</i> v. <i>Director of Enforcement, Foreign Exchange Regulation Act<a href="#_ftn124" name="_ftnref124"><b>[124]</b></a></i>, where the judge observed that "the expression 'reason to believe' is not synonymous with subjective satisfaction of the officer. The belief must be held in good faith; it cannot be merely a pretence….."<a href="#_ftn125" name="_ftnref125">[125]</a></p>
<p style="text-align: justify; ">In light of this, the judge in <i>Balbir Singh </i>remarked that "whether there was such reason to believe and whether the officer empowered acted in a bona fide manner, depends upon the facts and circumstances of the case and will have a bearing in appreciation of the evidence" <a href="#_ftn126" name="_ftnref126">[126]</a>. The standard considered by the court in <i>Balbir Singh </i>and <i>Partap Singh</i> is different from the 'reasonable suspicion' or 'reasonable grounds' standard as per Section 41 and 165 of Cr PC. But I think the discussion can help to inform our analysis of the idea of reasonableness in law enforcement actions. Of importance was the court requirement of something more than mere "pretence" as well as a belief held in good faith. This could suggest that in fact the reasoning in American jurisprudence about reasonable suspicion might be at least somewhat similar to how Indian courts view reasonable suspicion or grounds in the context of predictive policing, and therefore how we could similarly conjecture that predictive evidence could form part of the reasonable suspicion calculus in India as well.</p>
<h4 style="text-align: justify; ">Difference in judicial treatment of illegally obtained evidence - Indian lack of exclusionary rules</h4>
<p style="text-align: justify; ">However, the apparent similarity of how police in America and India may act in non-warrant situations - guided by the idea of reasonable suspicion - is only veneered by linguistic parallels. Despite the existence of such conditions which govern the searches without a warrant, I believe that Indian courts currently may provide far less protection against unlawful use of predictive technologies. The main premise behind this argument is that Indian courts refuse to exclude evidence that was obtained in breaches of the conditions of sections of the Cr PC. What exists in place of evidentiary safeguards is a line of cases in which courts routinely admit unlawfully or illegally obtained evidence. Without protections against unlawfully gathered evidence being considered relevant by courts, any regulations on search or conditions to be met before a search is lawful become ineffective. Evidence may simply enter the courtroom through a backdoor.</p>
<p style="text-align: justify; ">In the USA, this is by and large, not the case. Although there are exceptions to these rules, exclusionary rules are set out to prevent admission of evidence which violates the constitution<a href="#_ftn127" name="_ftnref127">[127]</a>. "The exclusionary rule applies to evidence gained from an unreasonable search or seizure in violation of the Fourth Amendment "<a href="#_ftn128" name="_ftnref128">[128]</a>. Mapp v. Ohio <a href="#_ftn129" name="_ftnref129">[129]</a> set the precedent for excluding unconstitutionally gathered evidence, where the court ruled that "all evidence obtained by searches and seizures in violation of the Federal Constitution is inadmissible in a criminal trial in a state court" <a href="#_ftn130" name="_ftnref130">[130]</a>.</p>
<p style="text-align: justify; ">Any such evidence which then leads law enforcement to collect new information may also be excluded, as part of the "fruit of the poisonous tree" doctrine<a href="#_ftn131" name="_ftnref131">[131]</a>, established in Silverthorne Lumber Co. v. United States <a href="#_ftn132" name="_ftnref132">[132]</a>. The doctrine is a metaphor which suggests that if the source of certain evidence is tainted, so is 'fruit' or derivatives from that unconstitutional evidence. One such application was in <i>Beck v. Ohio<a href="#_ftn133" name="_ftnref133"><b>[133]</b></a></i>, where the courts overturned a petitioner's conviction because the evidence used to convict him was obtained via an unlawful arrest.</p>
<p style="text-align: justify; ">However in India's context, there is very little protection against the admission and use of unlawfully gathered evidence. In fact, there are a line of cases which lay out the extent of consideration given to unlawfully gathered evidence - both cases that specifically deal with the rules as per the Indian Cr PC as well as cases from other contexts - which follow and develop this line of reasoning of allowing illegally obtained evidence.</p>
<p style="text-align: justify; ">One case to pay attention to is <i>State of Maharastra v. Natwarlal Damodardas Soni</i> - in this case, the Anti-Corruption Bureau searched the house of the accused after receiving certain information as a tip. The police "had powers under the Code of Criminal Procedure to search and seize this gold if they had reason to believe that a cognizable offence had been committed in respect thereof"<a href="#_ftn134" name="_ftnref134">[134]</a>. Justice Sarkaria, in delivering his judgement, observed that for argument's sake, even if the search was illegal, "then also, it will not affect the validity of the seizure and further investigation"<a href="#_ftn135" name="_ftnref135">[135]</a>. The judge drew reasoning from <i>Radhakishan v. State of U.P</i><a href="#_ftn136" name="_ftnref136">[136]</a>. This which was a case involving a postman who had certain postal items that were undelivered recovered from his house. As the judge in <i>Radhakishan</i> noted:</p>
<p style="text-align: justify; ">"So far as the alleged illegality of the search is concerned, it is sufficient to say that even assuming that the search was illegal the seizure of the articles is not vitiated. It may be that where the provisions of Sections 103 and 165 of the Code of Criminal Procedure, are contravened the search could be resisted by the person whose premises are sought to be searched. It may also be that because of the illegality of the search the court may be inclined to examine carefully the evidence regarding the seizure. But beyond these two consequences no further consequence ensues." <a href="#_ftn137" name="_ftnref137">[137]</a></p>
<p style="text-align: justify; "><i>Shyam Lal Sharma</i> v. <i>State of M.P.<a href="#_ftn138" name="_ftnref138"><b>[138]</b></a></i> was also drawn upon, where it was held that "even if the search is illegal being in contravention with the requirements of Section 165 of the Criminal Procedure Code, 1898, that provision ceases to have any application to the subsequent steps in the investigation"<a href="#_ftn139" name="_ftnref139">[139]</a>.</p>
<p style="text-align: justify; ">Even in <i>Gulab Chand </i><i>Upadhyay</i>, mentioned above, the presiding judge contended that even "if arrest is made, it does not require any, much less strong, reasons to be recorded or reported by the police. Thus so long as the information or suspicion of cognizable offence is "reasonable" or "credible", the police officer is not accountable for the discretion of arresting or no arresting"<a href="#_ftn140" name="_ftnref140">[140]</a>.</p>
<p style="text-align: justify; ">A more complete articulation of the receptiveness of Indian courts to admit illegally gathered evidence can be seen in the aforementioned <i>Balbir Singh. </i>The judgement aimed to:</p>
<p style="text-align: justify; ">"dispose of one of the contentions that failure to comply with the provisions of Cr PC in respect of search and seizure even up to that stage would also vitiate the trial. This aspect has been considered in a number of cases and it has been held that the violation of the provisions particularly that of Sections 100, 102, 103 or 165 Cr PC strictly per se does not vitiate the prosecution case. If there is such violation, what the courts have to see is whether any prejudice was caused to the accused and in appreciating the evidence and other relevant factors, the courts should bear in mind that there was such a violation and from that point of view evaluate the evidence on record."<a href="#_ftn141" name="_ftnref141">[141]</a></p>
<p style="text-align: justify; ">The judges then consulted a series of authorities on the failure to comply with provisions of the Cr PC:</p>
<ol>
<li><i>State of Punjab</i> v. <i>Wassan Singh</i><a href="#_ftn142" name="_ftnref142">[142]</a><i>:</i> "irregularity in a search cannot vitiate the seizure of the articles"<a href="#_ftn143" name="_ftnref143">[143]</a>.</li>
<li style="text-align: justify; "><i>Sunder Singh</i> v. <i>State of U.P</i><a href="#_ftn144" name="_ftnref144">[144]</a><i>:</i> 'irregularity cannot vitiate the trial unless the accused has been prejudiced by the defect and it is also held that if reliable local witnesses are not available the search would not be vitiated."<a href="#_ftn145" name="_ftnref145">[145]</a></li>
<li style="text-align: justify; "><i>Matajog Dobey</i> v.<i>H.C. Bhari</i><a href="#_ftn146" name="_ftnref146">[146]</a><i>:</i> "when the salutory provisions have not been complied with, it may, however, affect the weight of the evidence in support of the search or may furnish a reason for disbelieving the evidence produced by the prosecution unless the prosecution properly explains such circumstance which made it impossible for it to comply with these provisions."<a href="#_ftn147" name="_ftnref147">[147]</a></li>
<li style="text-align: justify; "><i>R</i> v. <i>Sang</i><a href="#_ftn148" name="_ftnref148">[148]</a>: "reiterated the same principle that if evidence was admissible it matters not how it was obtained."<a href="#_ftn149" name="_ftnref149">[149]</a> Lord Diplock, one of the Lords adjudicating the case, observed that "however much the judge may dislike the way in which a particular piece of evidence was obtained before proceedings were commenced, if it is admissible evidence probative of the accused's guilt "it is no part of his judicial function to exclude it for this reason". <a href="#_ftn150" name="_ftnref150">[150]</a> As the judge in <i>Balbir Singh</i> quoted from Lord Diplock, a judge "has no discretion to refuse to admit relevant admissible evidence on the ground that it was obtained by improper or unfair means. The court is not concerned with how it was obtained."<a href="#_ftn151" name="_ftnref151">[151]</a></li>
</ol>
<p style="text-align: justify; ">The vast body of case law presented above provides observers with a clear image of the courts willingness to admit and consider illegally obtained evidence. The lack of safeguards against admission of unlawful evidence are important from the standpoint of preventing the excessive or unlawful use of predictive policing methods. The affronts to justice and privacy, as well as the risks of profiling, seem to become magnified when law enforcement use predictive methods more than just to augment their policing techniques but to replace some of them. The efficacy and expediency offered by using predictive policing needs to be balanced against the competing interest of ensuring rule of law and due process. In the Indian context, it seems courts sparsely consider this competing interest.</p>
<p style="text-align: justify; ">Naturally, weighing in on which approach is better depends on a multitude of criteria like context, practicality, societal norms and many more. It also draws on existing debates in administrative law about the role of courts, which may emphasise protecting individuals and preventing excessive state power (red light theory) or emphasise efficiency in the governing process with courts assisting the state to achieve policy objectives (green light theory) <a href="#_ftn152" name="_ftnref152">[152]</a>.</p>
<p style="text-align: justify; ">A practical response may be that India should aim to embrace both elements and balance them appropriately, although what an appropriate balance again may vary. There are some who claim that this balance already exists in India. Evidence for such a claim may come from <i>R.M. Malkani v. State of Maharashtra</i><a href="#_ftn153" name="_ftnref153">[153]</a>, where the court considered whether an illegally tape-recorded conversation<i> </i>could be admissible. In its reasoning, the court drew from <i>Kuruma, Son of Kanju v. R.</i> <a href="#_ftn154" name="_ftnref154">[154]</a><i>, </i>noting that</p>
<p style="text-align: justify; "><i>"</i> if evidence was admissible it matters not how it was obtained. There is of course always a word of caution. It is that the Judge has a discretion to disallow evidence in a criminal case if the strict rules of admissibility would operate unfairly against the accused. That caution is the golden rule in criminal jurisprudence"<a href="#_ftn155" name="_ftnref155">[155]</a>.</p>
<p style="text-align: justify; ">While this discretion exists at least principally in India, in practice the cases presented above show that judges rarely exercise that discretion to prevent or bar the admission of illegally obtained evidence or evidence that was obtained in a manner that infringed the provisions governing search or arrest in the Cr PC. Indeed, the concern is that perhaps the necessary safeguards required to keep law enforcement practices, including predictive policing techniques, in check would be better served by a greater focus on reconsidering the legality of unlawfully gathered evidence. If not, evidence which should otherwise be inadmissible may find its way into consideration by existing legal backdoors.</p>
<h3 style="text-align: justify; ">Risk of discriminatory predictive analysis</h3>
<p style="text-align: justify; ">Regarding the risk of discriminatory profiling, Article 15 of India's Constitution<a href="#_ftn156" name="_ftnref156">[156]</a> states that "the State shall not discriminate against any citizen on grounds only of religion, race, caste, sex, place of birth or any of them" <a href="#_ftn157" name="_ftnref157">[157]</a>. The existence of constitutional protection for such forms of discrimination suggests that India will be able to guard against discriminatory predictive policing. However, as mentioned before, predictive analytics often discriminates institutionally, "whereby unconscious implicit biases and inertia within society's institutions account for a large part of the disparate effects observed, rather than intentional choices"<a href="#_ftn158" name="_ftnref158">[158]</a>. As in most jurisdictions, preventing these forms of discrimination are much harder. Especially in a jurisdiction whose courts are already receptive to allowing admission of illegally obtained evidence, the risk of discriminatory data mining or prejudiced algorithms being used by police becomes magnified. Because the discrimination may be unintentional, it may be even harder for evidence from discriminatory predictive methods to be scrutinised or when applicable, dismissed by the courts.</p>
<h2 style="text-align: justify; ">Conclusion for India</h2>
<p style="text-align: justify; ">One thing which is eminently clear from the analysis of possible interpretations of predictive evidence is that Indian Courts have had no experience with any predictive policing cases, because the technology itself is still at a nascent stage. There is in fact a long way to go before predictive policing will become used on a scale similar to that of USA for example.</p>
<p style="text-align: justify; ">But, even in places where predictive policing is used much more prominently, there is no precedent to observe how courts may view predictive policing. Ferguson's method of locating analogous situations to predictive policing which courts have already considered is one notable approach, but even this does not provide complete answer. One of his main conclusions that predictive policing will affect the reasonable suspicion calculus, or in India's case, contribute to 'reasonable grounds' in some ways, is perhaps the most valid one.</p>
<p style="text-align: justify; ">However, what provides more cause for concern in India's context are the limited protections against use of unlawfully gathered evidence. The lack of 'exclusionary rules' unlike those present in the US amplifies the various risks of predictive policing because individuals have little means of redress in such situations where predictive policing may be used unjustly against them.</p>
<p style="text-align: justify; ">Yet, the promise of predictive policing remains undeniably attractive for India. The successes predictive policing methods seem to have had In the US and UK coupled with the more efficient allocation of law enforcement's resources as a consequence of adapting predictive policing evidence this point. The government recognises this and seems to be laying the foundation and basic digital infrastructure required to utilize predictive policing optimally. One ought also to ask whether it is the even within the court's purview to decide what kind of policing methods are to be permissible through evaluating the nature of evidence. There is a case to be made for the legislative arm of the state to provide direction on how predictive policing is to be used in India. Perhaps the law must also evolve with the changes in technology, especially if courts are to scrutinise the predictive policing methods themselves.</p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1">[1]</a> Joh, Elizabeth E. "Policing by Numbers: Big Data and the Fourth Amendment." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 1, 2014. http://papers.ssrn.com/abstract=2403028. <br /> <br /></p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2">[2]</a> Tene, Omer, and Jules Polonetsky. "Big Data for All: Privacy and User Control in the Age of Analytics." Northwestern Journal of Technology and Intellectual Property 11, no. 5 (April 17, 2013): 239.</p>
</div>
<div id="ftn3">
<p><a href="#_ftnref3" name="_ftn3">[3]</a> Datta, Rajbir Singh. "Predictive Analytics: The Use and Constitutionality of Technology in Combating Homegrown Terrorist Threats." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 1, 2013. http://papers.ssrn.com/abstract=2320160.</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4">[4]</a> Johnson, Jeffrey Alan. "Ethics of Data Mining and Predictive Analytics in Higher Education." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 8, 2013. http://papers.ssrn.com/abstract=2156058.</p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5">[5]</a> Ibid.</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6">[6]</a> Duhigg, Charles. "How Companies Learn Your Secrets." The New York Times, February 16, 2012. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.</p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7">[7]</a> Ibid.</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8">[8]</a> Lijaya, A, M Pranav, P B Sarath Babu, and V R Nithin. "Predicting Movie Success Based on IMDB Data." International Journal of Data Mining Techniques and Applications 3 (June 2014): 365-68.</p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9"></a></p>
<p>[9] Johnson, Jeffrey Alan. "Ethics of Data Mining and Predictive Analytics in Higher Education." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 8, 2013. http://papers.ssrn.com/abstract=2156058.</p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10">[10]</a> Sangvinatsos, Antonios A. "Explanatory and Predictive Analysis of Corporate Bond Indices Returns." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, June 1, 2005. http://papers.ssrn.com/abstract=891641.</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11">[11]</a> Barocas, Solon, and Andrew D. Selbst. "Big Data's Disparate Impact." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 13, 2015. http://papers.ssrn.com/abstract=2477899.</p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12">[12]</a> Joh, supra note 1.</p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13">[13]</a> US Environmental Protection Agency. "How We Use Data in the Mid-Atlantic Region." US EPA. Accessed November 6, 2015. http://archive.epa.gov/reg3esd1/data/web/html/.</p>
</div>
<div id="ftn14">
<p><a href="#_ftnref14" name="_ftn14">[14]</a> See <a href="http://web.archive.org/web/20060603014844/http:/blog.wired.com/27BStroke6/att_klein_wired.pdf">here</a> for details of blackroom.</p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15">[15]</a> Joh, supra note 1, at pg 48.</p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16">[16]</a> Perry, Walter L., Brian McInnis, Carter C. Price, Susan Smith and John S. Hollywood. Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica, CA: RAND Corporation, 2013. http://www.rand.org/pubs/research_reports/RR233. Also available in print form.</p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17">[17]</a> Ibid, at pg 2.</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18">[18]</a> Chan, Sewell. "Why Did Crime Fall in New York City?" City Room. Accessed November 6, 2015. http://cityroom.blogs.nytimes.com/2007/08/13/why-did-crime-fall-in-new-york-city/.</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19">[19]</a> Bureau of Justice Assistance. "COMPSTAT: ITS ORIGINS, EVOLUTION, AND FUTURE IN LAW ENFORCEMENT AGENCIES," 2013. http://www.policeforum.org/assets/docs/Free_Online_Documents/Compstat/compstat%20-%20its%20origins%20evolution%20and%20future%20in%20law%20enforcement%20agencies%202013.pdf.</p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20">[20]</a> 1996 internal NYPD article "Managing for Results: Building a Police Organization that Dramatically Reduces Crime, Disorder, and Fear."</p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21">[21]</a> Bratton, William. "Crime by the Numbers." The New York Times, February 17, 2010. http://www.nytimes.com/2010/02/17/opinion/17bratton.html.</p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22">[22]</a> RAND CORP, supra note 16.</p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23">[23]</a> RAND CORP, supra note 16, at pg 19.</p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24">[24]</a> Joh, supra note 1, at pg 44.</p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25">[25]</a> RAND CORP, supra note 16, pg 38.</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26">[26]</a> Ibid.</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27">[27]</a> RAND CORP, supra note 16, at pg 39.</p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28">[28]</a> Ibid.</p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29">[29]</a> RAND CORP, supra note 16, at pg 41.</p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30">[30]</a> Data-Smart City Solutions. "Dr. George Mohler: Mathematician and Crime Fighter." Data-Smart City Solutions, May 8, 2013. http://datasmart.ash.harvard.edu/news/article/dr.-george-mohler-mathematician-and-crime-fighter-166.</p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31">[31]</a> RAND CORP, supra note 16, at pg 44.</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32">[32]</a> Joh, supra note 1, at pg 45.</p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33">[33]</a> Ouellette, Danielle. "Dispatch - A Hot Spots Experiment: Sacramento Police Department," June 2012. http://cops.usdoj.gov/html/dispatch/06-2012/hot-spots-and-sacramento-pd.asp.</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34">[34]</a> Pitney Bowes Business Insight. "The Safer Derbyshire Partnership." Derbyshire, 2013. http://www.mapinfo.com/wp-content/uploads/2013/05/safer-derbyshire-casestudy.pdf.</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35">[35]</a> Ibid.</p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36">[36]</a> Daniel B Neill, Wilpen L. Gorr. "Detecting and Preventing Emerging Epidemics of Crime," 2007.</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37">[37]</a> RAND CORP, supra note 16, at pg 33.</p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38">[38]</a> Joh, supra note 1, at pg 46.</p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39">[39]</a> Paul, Jeffery S, and Thomas M. Joiner. "Integration of Centralized Intelligence with Geographic Information Systems: A Countywide Initiative." Geography and Public Safety 3, no. 1 (October 2011): 5-7.</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40">[40]</a> Mohler, supra note 30.</p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41">[41]</a> Ibid.</p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42">[42]</a> Moses, B., Lyria, & Chan, J. (2014). Using Big Data for Legal and Law Enforcement <br /> Decisions: Testing the New Tools (SSRN Scholarly Paper No. ID 2513564). Rochester, NY: Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2513564</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43">[43]</a> Gorner, Jeremy. "Chicago Police Use Heat List as Strategy to Prevent Violence." Chicago Tribune. August 21, 2013. http://articles.chicagotribune.com/2013-08-21/news/ct-met-heat-list-20130821_1_chicago-police-commander-andrew-papachristos-heat-list.</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44">[44]</a> Stroud, Matt. "The Minority Report: Chicago's New Police Computer Predicts Crimes, but Is It Racist?" The Verge. Accessed November 13, 2015. http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist.</p>
</div>
<div id="ftn45">
<p><a href="#_ftnref45" name="_ftn45">[45]</a> Moser, Whet. "The Small Social Networks at the Heart of Chicago Violence." Chicago Magazine, December 9, 2013. http://www.chicagomag.com/city-life/December-2013/The-Small-Social-Networks-at-the-Heart-of-Chicago-Violence/.</p>
</div>
<div id="ftn46">
<p><a href="#_ftnref46" name="_ftn46">[46]</a> Lester, Aaron. "Police Clicking into Crimes Using New Software." Boston Globe, March 18, 2013. https://www.bostonglobe.com/business/2013/03/17/police-intelligence-one-click-away/DzzDbrwdiNkjNMA1159ybM/story.html.</p>
</div>
<div id="ftn47">
<p><a href="#_ftnref47" name="_ftn47">[47]</a> Stanley, Jay. "Chicago Police 'Heat List' Renews Old Fears About Government Flagging and Tagging." American Civil Liberties Union, February 25, 2014. https://www.aclu.org/blog/chicago-police-heat-list-renews-old-fears-about-government-flagging-and-tagging.</p>
</div>
<div id="ftn48">
<p><a href="#_ftnref48" name="_ftn48">[48]</a> Rieke, Aaron, David Robinson, and Harlan Yu. "Civil Rights, Big Data, and Our Algorithmic Future," September 2014. https://bigdata.fairness.io/wp-content/uploads/2015/04/2015-04-20-Civil-Rights-Big-Data-and-Our-Algorithmic-Future-v1.2.pdf.</p>
</div>
<div id="ftn49">
<p><a href="#_ftnref49" name="_ftn49">[49]</a> Edmond, Deepu Sebastian. "Jhakhand's Digital Leap." Indian Express, September 15, 2013. http://www.jhpolice.gov.in/news/jhakhands-digital-leap-indian-express-15092013-18219-1379316969.</p>
</div>
<div id="ftn50">
<p><a href="#_ftnref50" name="_ftn50">[50]</a> Jharkhand Police. "Jharkhand Police IT Vision 2020 - Effective Shared Open E-Governance." 2012. http://jhpolice.gov.in/vision2020. See slide 2</p>
<p><a href="#_ftnref51" name="_ftn51">[51]</a> Edmond, supra note 49.</p>
</div>
<div id="ftn52">
<p><a href="#_ftnref52" name="_ftn52">[52]</a> Edmond, supra note 49.</p>
</div>
<div id="ftn53">
<p><a href="#_ftnref53" name="_ftn53">[53]</a> Kumar, Raj. "Enter, the Future of Policing - Cops to Team up with IIM Analysts to Predict & Prevent Incidents." The Telegraph. August 28, 2012. http://www.telegraphindia.com/1120828/jsp/jharkhand/story_15905662.jsp#.VkXwxvnhDWK.</p>
<p><a href="#_ftnref54" name="_ftn54">[54]</a> Ibid.</p>
</div>
<div id="ftn54"></div>
<div id="ftn55">
<p><a href="#_ftnref55" name="_ftn55">[55]</a> Ibid.</p>
</div>
<div id="ftn56">
<p><a href="#_ftnref56" name="_ftn56">[56]</a> Ibid.</p>
</div>
<div id="ftn57">
<p><a href="#_ftnref57" name="_ftn57">[57]</a> See supra note 49.</p>
</div>
<div id="ftn58">
<p><a href="#_ftnref58" name="_ftn58">[58]</a> See <a href="http://dashboard.jhpolice.gov.in/">here</a> for Jharkhand Police crime dashboard.</p>
</div>
<div id="ftn59">
<p><a href="#_ftnref59" name="_ftn59">[59]</a> Lavanya Gupta, and Selva Priya. "Predicting Crime Rates for Predictive Policing." Gandhian Young Technological Innovation Award, December 29, 2014. http://gyti.techpedia.in/project-detail/predicting-crime-rates-for-predictive-policing/3545.</p>
</div>
<div id="ftn60">
<p><a href="#_ftnref60" name="_ftn60">[60]</a> Gupta, Lavanya. "Minority Report: Minority Report." Accessed November 13, 2015. http://cmuws2014.blogspot.in/2015/01/minority-report.html.</p>
</div>
<div id="ftn61">
<p><a href="#_ftnref61" name="_ftn61">[61]</a> See supra note 59.</p>
</div>
<div id="ftn62">
<p><a href="#_ftnref62" name="_ftn62">[62]</a> See <a href="http://bprd.nic.in/showfile.asp?lid=1224">here</a> for details about 44th All India Police Science Congress.</p>
</div>
<div id="ftn63">
<p><a href="#_ftnref63" name="_ftn63">[63]</a> India, Press Trust of. "Police Science Congress in Gujarat to Have DRDO Exhibition." Business Standard India, March 10, 2015. http://www.business-standard.com/article/pti-stories/police-science-congress-in-gujarat-to-have-drdo-exhibition-115031001310_1.html.</p>
</div>
<div id="ftn64">
<p><a href="#_ftnref64" name="_ftn64">[64]</a> National Crime Records Bureau. "About Crime and Criminal Tracking Network & Systems - CCTNS." Accessed November 13, 2015. http://ncrb.gov.in/cctns.htm.</p>
</div>
<div id="ftn65">
<p><a href="#_ftnref65" name="_ftn65">[65]</a> Ibid. (See index page)</p>
</div>
<div id="ftn66">
<p><a href="#_ftnref66" name="_ftn66">[66]</a> U.S. Const. amend. IV, available <a href="https://www.law.cornell.edu/constitution/fourth_amendment">here</a></p>
</div>
<div id="ftn67">
<p><a href="#_ftnref67" name="_ftn67">[67]</a> United States v Katz, 389 U.S. 347 (1967) , see <a href="https://supreme.justia.com/cases/federal/us/389/347/case.html">here</a></p>
</div>
<div id="ftn68">
<p><a href="#_ftnref68" name="_ftn68">[68]</a> See supra note 1, at pg 60.</p>
</div>
<div id="ftn69">
<p><a href="#_ftnref69" name="_ftn69">[69]</a> See supra note 1, at pg 60.</p>
</div>
<div id="ftn70">
<p><a href="#_ftnref70" name="_ftn70">[70]</a> Villasenor, John. "What You Need to Know about the Third-Party Doctrine." The Atlantic, December 30, 2013. http://www.theatlantic.com/technology/archive/2013/12/what-you-need-to-know-about-the-third-party-doctrine/282721/.</p>
</div>
<div id="ftn71">
<p><a href="#_ftnref71" name="_ftn71">[71]</a> Smith v Maryland, 442 U.S. 735 (1979), see <a href="https://supreme.justia.com/cases/federal/us/442/735/case.html">here</a></p>
</div>
<div id="ftn72">
<p><a href="#_ftnref72" name="_ftn72">[72]</a> United States v Jones, 565 U.S. ___ (2012), see <a href="https://supreme.justia.com/cases/federal/us/565/10-1259/">here</a></p>
</div>
<div id="ftn73">
<p><a href="#_ftnref73" name="_ftn73">[73]</a> Newell, Bryce Clayton. "Local Law Enforcement Jumps on the Big Data Bandwagon: Automated License Plate Recognition Systems, Information Privacy, and Access to Government Information." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, October 16, 2013. http://papers.ssrn.com/abstract=2341182, at pg 24.</p>
</div>
<div id="ftn74">
<p><a href="#_ftnref74" name="_ftn74">[74]</a> See supra note 72.</p>
</div>
<div id="ftn75">
<p><a href="#_ftnref75" name="_ftn75">[75]</a> Dahyabhai Chhaganbhai Thakker vs State Of Gujarat, 1964 AIR 1563</p>
</div>
<div id="ftn76">
<p><a href="#_ftnref76" name="_ftn76">[76]</a> See supra note 16.</p>
</div>
<div id="ftn77">
<p><a href="#_ftnref77" name="_ftn77">[77]</a> See supra note 66.</p>
</div>
<div id="ftn78">
<p><a href="#_ftnref78" name="_ftn78">[78]</a> Brinegar v. United States, 338 U.S. 160 (1949), see <a href="https://supreme.justia.com/cases/federal/us/338/160/case.html">here</a></p>
</div>
<div id="ftn79">
<p><a href="#_ftnref79" name="_ftn79">[79]</a> Terry v. Ohio, 392 U.S. 1 (1968), see <a href="https://supreme.justia.com/cases/federal/us/392/1/case.html">here</a></p>
</div>
<div id="ftn80">
<p><a href="#_ftnref80" name="_ftn80">[80]</a> Ferguson, Andrew Guthrie. "Big Data and Predictive Reasonable Suspicion." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, April 4, 2014. http://papers.ssrn.com/abstract=2394683, at pg 287. See also supra note 79.</p>
</div>
<div id="ftn81">
<p><a href="#_ftnref81" name="_ftn81">[81]</a> See supra note 80.</p>
</div>
<div id="ftn82">
<p><a href="#_ftnref82" name="_ftn82">[82]</a> See supra note 80.</p>
</div>
<div id="ftn83">
<p><a href="#_ftnref83" name="_ftn83">[83]</a> See supra note 80.</p>
</div>
<div id="ftn84">
<p><a href="#_ftnref84" name="_ftn84">[84]</a> See supra note 80, at pg 289.</p>
</div>
<div id="ftn85">
<p><a href="#_ftnref85" name="_ftn85">[85]</a> Illinois v. Gates, 462 U.S. 213 (1983). See <a href="https://supreme.justia.com/cases/federal/us/462/213/case.html">here</a></p>
</div>
<div id="ftn86">
<p><a href="#_ftnref86" name="_ftn86">[86]</a> See Alabama v. White, 496 U.S. 325 (1990). See <a href="https://supreme.justia.com/cases/federal/us/496/325/">here</a></p>
</div>
<div id="ftn87">
<p><a href="#_ftnref87" name="_ftn87">[87]</a> See supra note 80, at pg 291.</p>
</div>
<div id="ftn88">
<p><a href="#_ftnref88" name="_ftn88">[88]</a> See supra note 80, at pg 293.</p>
</div>
<div id="ftn89">
<p><a href="#_ftnref89" name="_ftn89">[89]</a> See supra note 80, at pg 308.</p>
</div>
<div id="ftn90">
<p><a href="#_ftnref90" name="_ftn90">[90]</a> Ibid.</p>
</div>
<div id="ftn91">
<p><a href="#_ftnref91" name="_ftn91">[91]</a> Ibid.</p>
</div>
<div id="ftn92">
<p><a href="#_ftnref92" name="_ftn92">[92]</a> Larissa Cespedes-Yaffar, Shayona Dhanak, and Amy Stephenson. "U.S. v. Mendenhall, U.S. v. Sokolow, and the Drug Courier Profile Evidence Controversy." Accessed July 6, 2015. http://courses2.cit.cornell.edu/sociallaw/student_projects/drugcourier.html.</p>
</div>
<div id="ftn93">
<p><a href="#_ftnref93" name="_ftn93">[93]</a> Ibid.</p>
</div>
<div id="ftn94">
<p><a href="#_ftnref94" name="_ftn94">[94]</a> United States v. Sokolow, 490 U.S. 1 (1989), see <a href="https://supreme.justia.com/cases/federal/us/490/1/">here</a></p>
</div>
<div id="ftn95">
<p><a href="#_ftnref95" name="_ftn95">[95]</a> See supra note 80, at pg 295.</p>
</div>
<div id="ftn96">
<p><a href="#_ftnref96" name="_ftn96">[96]</a> See supra note 80, at pg 297.</p>
</div>
<div id="ftn97">
<p><a href="#_ftnref97" name="_ftn97">[97]</a> See supra note 80, at pg 308.</p>
</div>
<div id="ftn98">
<p><a href="#_ftnref98" name="_ftn98">[98]</a> See supra note 80, at pg 310.</p>
</div>
<div id="ftn99">
<p><a href="#_ftnref99" name="_ftn99">[99]</a> See supra note 11.</p>
</div>
<div id="ftn100">
<p><a href="#_ftnref100" name="_ftn100">[100]</a> See supra note 11.</p>
</div>
<div id="ftn101">
<p><a href="#_ftnref101" name="_ftn101"><sup><sup>[101]</sup></sup></a> <sup> </sup> See supra note 80, at pg 303.</p>
</div>
<div id="ftn102">
<p><a href="#_ftnref102" name="_ftn102">[102]</a> See supra note 80, at pg 300.</p>
</div>
<div id="ftn103">
<p><a href="#_ftnref103" name="_ftn103">[103]</a> Illinois v. Wardlow, 528 U.S. 119 (2000), see <a href="https://supreme.justia.com/cases/federal/us/528/119/case.html">here</a></p>
</div>
<div id="ftn104">
<p><a href="#_ftnref104" name="_ftn104">[104]</a> Ibid.</p>
</div>
<div id="ftn105">
<p><a href="#_ftnref105" name="_ftn105">[105]</a> See supra note 80, at pg 301.</p>
</div>
<div id="ftn106">
<p><a href="#_ftnref106" name="_ftn106">[106]</a> Ibid.</p>
</div>
<div id="ftn107">
<p><a href="#_ftnref107" name="_ftn107">[107]</a> See supra note 1, at pg 42.</p>
</div>
<div id="ftn108">
<p><a href="#_ftnref108" name="_ftn108">[108]</a> See supra note 80, at pg 303.</p>
</div>
<div id="ftn109">
<p><a href="#_ftnref109" name="_ftn109">[109]</a> See supra note 80, at pg 303.</p>
</div>
<div id="ftn110">
<p><a href="#_ftnref110" name="_ftn110">[110]</a> Ibid.</p>
</div>
<div id="ftn111">
<p><a href="#_ftnref111" name="_ftn111">[111]</a> Ibid.</p>
</div>
<div id="ftn112">
<p><a href="#_ftnref112" name="_ftn112">[112]</a> Ibid.</p>
</div>
<div id="ftn113">
<p><a href="#_ftnref113" name="_ftn113">[113]</a> See supra note 80, at pg 312.</p>
</div>
<div id="ftn114">
<p><a href="#_ftnref114" name="_ftn114">[114]</a> See supra note 80, at pg 317.</p>
</div>
<div id="ftn115">
<p><a href="#_ftnref115" name="_ftn115">[115]</a> See supra note 80, at pg 319.</p>
</div>
<div id="ftn116">
<p><a href="#_ftnref116" name="_ftn116">[116]</a> See supra note 80, at pg 321.</p>
</div>
<div id="ftn117">
<p><a href="#_ftnref117" name="_ftn117">[117]</a> Section 165 Indian Criminal Procedure Code, see <a href="http://indiankanoon.org/doc/996365/">here</a></p>
</div>
<div id="ftn118">
<p><a href="#_ftnref118" name="_ftn118">[118]</a> Gulab Chand Upadhyaya vs State Of U.P, 2002 CriLJ 2907</p>
</div>
<div id="ftn119">
<p><a href="#_ftnref119" name="_ftn119">[119]</a> Section 41 Indian Criminal Procedure Code</p>
</div>
<div id="ftn120">
<p><a href="#_ftnref120" name="_ftn120">[120]</a> See supra note 79</p>
</div>
<div id="ftn121">
<p><a href="#_ftnref121" name="_ftn121">[121]</a> State of Punjab v. Balbir Singh. (1994) 3 SCC 299</p>
</div>
<div id="ftn122">
<p><a href="#_ftnref122" name="_ftn122">[122]</a> Ibid.</p>
</div>
<div id="ftn123">
<p><a href="#_ftnref123" name="_ftn123">[123]</a> Section 41 and 42 in The Narcotic Drugs and Psychotropic Substances Act 1985, see <a href="http://indiankanoon.org/doc/1727139/">here</a></p>
</div>
<div id="ftn124">
<p><a href="#_ftnref124" name="_ftn124">[124]</a> <i>Partap Singh (Dr)</i> v. <i>Director of Enforcement, Foreign Exchange Regulation Act. </i>(1985) 3 SCC 72 : 1985 SCC (Cri) 312 : 1985 SCC (Tax) 352 : AIR 1985 SC 989</p>
</div>
<div id="ftn125">
<p><a href="#_ftnref125" name="_ftn125">[125]</a> Ibid, at SCC pg 77-78.</p>
</div>
<div id="ftn126">
<p><a href="#_ftnref126" name="_ftn126">[126]</a> See supra note 121, at pg 313.</p>
</div>
<div id="ftn127">
<p><a href="#_ftnref127" name="_ftn127">[127]</a> Carlson, Mr David. "Exclusionary Rule." LII / Legal Information Institute, June 10, 2009. https://www.law.cornell.edu/wex/exclusionary_rule.</p>
</div>
<div id="ftn128">
<p><a href="#_ftnref128" name="_ftn128">[128]</a> Ibid.</p>
</div>
<div id="ftn129">
<p><a href="#_ftnref129" name="_ftn129">[129]</a> Mapp v Ohio, 367 U.S. 643 (1961), see <a href="https://supreme.justia.com/cases/federal/us/367/643/case.html">here</a></p>
</div>
<div id="ftn130">
<p><a href="#_ftnref130" name="_ftn130">[130]</a> Ibid.</p>
</div>
<div id="ftn131">
<p><a href="#_ftnref131" name="_ftn131">[131]</a> Busby, John C. "Fruit of the Poisonous Tree." LII / Legal Information Institute, September 21, 2009. https://www.law.cornell.edu/wex/fruit_of_the_poisonous_tree.</p>
</div>
<div id="ftn132">
<p><a href="#_ftnref132" name="_ftn132">[132]</a> Silverthorne Lumber Co., Inc. v. United States, 251 U.S. 385 (1920), see <a href="https://supreme.justia.com/cases/federal/us/251/385/case.html">here</a>.</p>
</div>
<div id="ftn133">
<p><a href="#_ftnref133" name="_ftn133">[133]</a> Beck v. Ohio, 379 U.S. 89 (1964), see <a href="https://supreme.justia.com/cases/federal/us/379/89/case.html">here</a>.</p>
</div>
<div id="ftn134">
<p><a href="#_ftnref134" name="_ftn134">[134]</a> State of Maharashtra v. Natwarlal Damodardas Soni, (1980) 4 SCC 669, at 673.</p>
</div>
<div id="ftn135">
<p><a href="#_ftnref135" name="_ftn135">[135]</a> Ibid.</p>
</div>
<div id="ftn136">
<p><a href="#_ftnref136" name="_ftn136">[136]</a> Radhakishan v. State of U.P. [AIR 1963 SC 822 : 1963 Supp 1 SCR 408, 411, 412 : (1963) 1 Cri LJ 809]</p>
</div>
<div id="ftn137">
<p><a href="#_ftnref137" name="_ftn137">[137]</a> Ibid, at SCR pg 411-12.</p>
</div>
<div id="ftn138">
<p><a href="#_ftnref138" name="_ftn138">[138]</a> <i>Shyam Lal Sharma</i> v. <i>State of M.P</i>. (1972) 1 SCC 764 : 1974 SCC (Cri) 470 : AIR 1972 SC 886</p>
</div>
<div id="ftn139">
<p><a href="#_ftnref139" name="_ftn139">[139]</a> See supra note 135, at page 674.</p>
</div>
<div id="ftn140">
<p><a href="#_ftnref140" name="_ftn140">[140]</a> See supra note 119, at para. 10.</p>
</div>
<div id="ftn141">
<p><a href="#_ftnref141" name="_ftn141">[141]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn142">
<p><a href="#_ftnref142" name="_ftn142">[142]</a> State of Punjab v. Wassan Singh, (1981) 2 SCC 1 : 1981 SCC (Cri) 292</p>
</div>
<div id="ftn143">
<p><a href="#_ftnref143" name="_ftn143">[143]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn144">
<p><a href="#_ftnref144" name="_ftn144">[144]</a> Sunder Singh v. State of U.P, AIR 1956 SC 411 : 1956 Cri LJ 801</p>
</div>
<div id="ftn145">
<p><a href="#_ftnref145" name="_ftn145">[145]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn146">
<p><a href="#_ftnref146" name="_ftn146">[146]</a> Matajog Dobey v.H.C. Bhari, AIR 1956 SC 44 : (1955) 2 SCR 925 : 1956 Cri LJ 140</p>
</div>
<div id="ftn147">
<p><a href="#_ftnref147" name="_ftn147">[147]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn148">
<p><a href="#_ftnref148" name="_ftn148">[148]</a> R v. Sang, (1979) 2 All ER 1222, 1230-31</p>
</div>
<div id="ftn149">
<p><a href="#_ftnref149" name="_ftn149">[149]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn150">
<p><a href="#_ftnref150" name="_ftn150">[150]</a> Ibid.</p>
</div>
<div id="ftn151">
<p><a href="#_ftnref151" name="_ftn151">[151]</a> Ibid.</p>
</div>
<div id="ftn152">
<p><a href="#_ftnref152" name="_ftn152">[152]</a> Harlow, Carol, and Richard Rawlings. <i>Law and Administration</i>. 3rd ed. Law in Context. Cambridge University Press, 2009.</p>
</div>
<div id="ftn153">
<p><a href="#_ftnref153" name="_ftn153">[153]</a> <i>R.M. Malkani v. State of Maharashtra,</i> (1973) 1 SCC 471</p>
</div>
<div id="ftn154">
<p><a href="#_ftnref154" name="_ftn154">[154]</a> Kuruma, Son of Kanju v. R., (1955) AC 197</p>
</div>
<div id="ftn155">
<p><a href="#_ftnref155" name="_ftn155">[155]</a> See supra note 154, at 477.</p>
</div>
<div id="ftn156">
<p><a href="#_ftnref156" name="_ftn156">[156]</a> Indian Const. Art 15, see <a href="http://indiankanoon.org/doc/609295/">here</a></p>
</div>
<div id="ftn157">
<p><a href="#_ftnref157" name="_ftn157">[157]</a> Ibid.</p>
</div>
<div id="ftn158">
<p><a href="#_ftnref158" name="_ftn158">[158]</a> See supra note 11.</p>
</div>
</div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications'>http://editors.cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications</a>
</p>
No publisherRohan GeorgeInternet GovernanceBig DataPrivacy2015-11-24T16:31:41ZBlog EntryIs India's Digital Health System Foolproof?
http://editors.cis-india.org/raw/is-indias-digital-health-system-foolproof
<b>This contribution by Aayush Rathi builds on "Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?" (by Aayush Rathi and Ambika Tandon, EPW Engage, Vol. 54, Issue No. 6, 09 Feb, 2019) and seeks to understand the role that state-run reproductive health portals such as the Mother and Child Tracking System (MCTS) and the Reproductive and Child Health will play going forward. The article critically outlines the overall digitised health information ecosystem being envisioned by the Indian state.</b>
<p> </p>
<h4>This article was first published in <a href="https://www.epw.in/engage/article/indias-digital-health-paradigm-foolproof" target="_blank">EPW Engage, Vol. 54, Issue No. 47</a>, on November 30, 2019</h4>
<hr />
<p>Introduced in 2013 and subsequently updated in 2016, the Ministry of Health and Family Welfare (MHFW) published a document laying out the standards for electronic health records (EHRs). While there exist varying interpretations of what constitutes as EHRs, some of its characteristics include electronic medical records (EMRs) of individual patients, arrangement of these records in a time series, and inter-operable linkages of the EMRs across various healthcare settings (Häyrinen et al 2008; OECD 2013).</p>
<p>To work effectively, EHRs are required to be highly interoperable so that they can facilitate exchange among health information systems (HIS) across participating hospitals. For this, the Integrated Health Information Platform (IHIP) is being developed so as to assimilate data from various registries across India and provide real-time information on health surveillance (Krishnamurthy 2018).</p>
<h3><strong>EHR Implementation: Unpacking the (Dis)incentive Structure</strong></h3>
<p>As the implementation of EHR standards is voluntary, anecdotal evidence indicates that their uptake in the Indian healthcare sector has been very slow. Here, the opposition of the Indian Medical Association to the Clinical Establishments (Registration and Regulation) Act, 2010, resulting in nationwide protests and subsequent legal challenges to the act, is instructive. To start with, the act prescribes the minimum standards that have to be maintained by clinical establishments which are registered or seeking registration (itself mandatory to run a clinic under the act) <strong>[1]</strong>. Further, Rule 9(ii) of the Clinical Establishments (Registration and Regulation) Rules, 2012, drafted under the act, requires clinical establishments to maintain EMRs or EHRs for every patient. However, with health being a state subject in India, the act has only been enforced in 11 states and all union territories except the National Capital Territory of Delhi (Jyoti 2018). The resistance to the act is largely due to protests by stakeholders from within the medical fraternity regarding its adverse impact on small- and medium-sized hospitals (Jyoti 2018).</p>
<h3><strong>Contextualising Clinicians' Inertia</strong></h3>
<p>Another major impediment to the adoption of EHRs by health service providers is reluctance on the part of individual physicians to transition to an EHR system. This is because compliance with EHR standards requires physicians to input clinical notes themselves.</p>
<p>Comparing the greater patient load faced by doctors in India vis-à-vis the United States (US), the chief medical officer of an EHR vendor in India estimates that the average Indian doctor sees about 40–60 patients a day, whereas in the US it may be around 18–20 patients (Kandhari 2017). This is suggestive of the wide disparity in the number of physicians per 1,000 citizens in both countries (World Bank nd). Given this, doctors in India tend to be more problem-oriented, time-strapped, and pay less attention to clinical notes (Kandhari 2017). Thus, clinicians will consider a system to be efficient only if the system reduces their documentation time, even if the time savings do not translate into better patient care (Allan and Englebright 2000). The inability of EHRs to help reduce documentation time deters clinicians from supporting their implementation (Poon et al 2004). Additionally, research done in the United States indicates that there is no evidence to suggest that an information system helps save time expended by clinicians on documentation (Daly et al 2002). Moreover, the use of an information system is stated to have had no impact on patient care, but doctors have acknowledged its use for research purposes (Holzemer and Henry 1992).</p>
<h3><strong>Prohibitive Costs of Implementation</strong></h3>
<p>While national-level EHRs have been adopted globally, their distribution across countries is telling. In a survey published in 2016 by the World Health Organization, wealthier countries were over-represented, with two-thirds from the upper-middle-income group and roughly half from the high-income countries having introduced EHR systems. On the other hand, only a third of lower-middle-income countries and 15% of low-income countries reported having implemented EHRs (World Health Organization 2016). A major reason for the slow uptake of EHRs in poorer countries is likely to be funding as EHR implementation requires considerable investment, with most projects averaging several million dollars (US) (Kuperman and Gibson 2003). Although various funding models for EHR implementation are being utilised globally, it is unclear what model will be adopted in India to bring in private healthcare service providers within its ambit (Healthcare Information and Management Systems Society 2007). This absence of funding direction for private actors poses to be a significant impediment in the integration of private databases with other public ones.</p>
<p>In general, poorer countries are also more likely to have less developed infrastructure and health Information and Communication Technology (ICT) to support EHR systems. Besides this, they not only lack the capacity and human resources required to develop and maintain such complex systems (Tierney et al 2010; McGinn et al 2011), but training periods have also been found to be long and more costly than expected (Kovener et al 1997).</p>
<h3><strong>Socio-economic Exclusions and Cross-cultural Barriers</strong></h3>
<p>There exists scant research investigating the existing use of EHRs in India, though preliminary work is being undertaken to assess EHR implementation in other developing countries (Tierney et al 2010; Fraser et al 2005). Even in the context of developed countries, where widespread adoption of EHRs has been gaining traction for some time now, very little data exists around implementation and efficacy in underserved regions and communities. This is further problematised as clinical information systems and user populations also vary in their characteristics and, for this reason, individual studies are unable to identify common trends that would predict EHR implementation success.</p>
<p>Underserved settings may lack the infrastructure needed to support EHRs. The risk of exclusion already exists in parts such as difficulties inherent in delivering care to remote locations, barriers related to cross-cultural communication, and the pervasive problem of providing care in the setting of severe resource constraints. Equally important is the fact that health workers who already report significant existing impediments in their delivery of routine care in these settings do not necessarily see EHRs as being useful in catering to the specific needs of their patient population (Bach et al 2004). Moreover, experience with EHRs also reveals that there are cultural barriers to capturing accurate data (Miklin et al 2019). What this could mean is that stigma associated with the diagnosis of conditions such as HIV/AIDS or induced abortions will result in their under-reporting even within EHR systems.</p>
<h3><strong>Stick or Twist?</strong></h3>
<p>Other modalities have been devised to nudge healthcare providers into adopting EHR standards voluntarily. The National Accreditation Board for Hospitals and Healthcare Providers (NABH), India, a constituent board of the Quality Council of India (a public–private initiative), has been reported to have incorporated the EHR standards within its accreditation matrix. NABH accreditation, considered an indicator of high quality patient care, is highly sought–after by hospitals in India in order to attract medical tourists as well as insurance companies: two prominent sources of income for hospitals (Kandhari 2017). Additionally, NABH accreditation is valid for a term of three years, thus requiring hospitals seeking to renew their accreditation to adopt EHR standards as well.</p>
<p>Another commercial use of EHR has been in health insurance. The Federation of Indian Chambers of Commerce and Industry (FICCI) and the Insurance Regulatory and Development Authority (IRDAI) have both voiced their support for expediting the implementation of the EHR standards (EMR Standards Committee 2013). Both, the FICCI and IRDAI have placed emphasis on adopting EHRs, seeing it as a necessary move for formalising the health insurance industry (FICCI 2015). They have also had representation on the committee that sent recommendations to the MHFW on the first version of the EHR standards in 2013 (FICCI 2015). FICCI had additionally played a coordination role in having the recommendations framed for the 2013 EHR standards.</p>
<h3><strong>Fluid Data Objectives</strong></h3>
<p>The push for EHR implementation is emblematic of a larger shift in the healthcare approach of the Indian state, that of an indirect targeting of demand-side financing by plugging data inefficiencies in health insurance.</p>
<p>The draft National Health Policy (NHP), published in 2015, reflected the mandate of the Ministry of Health and Family Welfare to strengthen the public health system by creating a right to healthcare legislation and reaching a public spend of 2.5% of the gross domestic product by 2018. The final version of the NHP, published in 2017, however, codified a shift in healthcare policy by focusing on strategic purchasing of secondary and tertiary care services from the private sector and a publicly funded health insurance model.</p>
<p>In line with the vision of the NHP 2017, in February 2018, the Union Minister for Finance and Corporate Affairs, Arun Jaitley, announced two major initiatives as a part of the government’s Ayushman Bharat programme (Ministry of Finance 2018). Administered under the aegis of the Ministry of Health and Family Welfare, these initiatives are intended to improve access to primary healthcare through the creation of 150,000 health and wellness centres as envisioned under the NHP 2017, and improve access to secondary and tertiary healthcare for over 100 million vulnerable families by providing insurance cover of up to ₹ 500,000 per family per year under the Pradhan Mantri–Rashtriya Swasthya Suraksha Mission/National Health Protection Scheme (PM–RSSM/NHPS) (Ministry of Health and Family Welfare 2018). The NHPS, modelled along the lines of the Affordable Care Act in the US, was later rebranded as the Pradhan Mantri–Jan Arogya Yojana (PM-JAY) at the time of its launch in September 2018. It is claimed to be the world’s largest government-funded healthcare programme and is intentioned to provide health insurance coverage for vulnerable sections in lieu of the Sustainable Development Goal-3 (National Health Authority nd).</p>
<p>To enable the implementation of the Ayushman Bharat programme, the NITI Aayog then proposed the creation of a supply-side digital infrastructure called National Health Stack (NHS) (NITI Aayog 2018). As outlined in the consultation and strategy paper, the NHS is “built for NHPS, but beyond NHPS.” The NHS seeks to leverage the digitisation push through IndiaStack, which seeks to digitalise “any large-scale health insurance program, in particular, any government-funded health care programs.” The synergy is clear, with the NHPS scheme also aiming to be “cashless and paperless at public hospitals and empanelled private hospitals" (National Health Authority nd) <strong>[2]</strong>.</p>
<p>The NHS is also closely aligned with the NHP 2017, which draws attention to leveraging technologies such as big data analytics on data stored in universal registries. The Vision document for the NHS emphasises the fragmented nature of health data as an impediment to reducing inequities in healthcare provision. The NHS, then, also seeks to be the master repository of health data akin to the IHIP. By creating a base layer of registries containing information about various actors involved in the healthcare supply chain (providers such as hospitals, beneficiaries, doctors, insurers and Accredited Social Health Activists), it potentially allows for recording of data from both public and private sector entities, plugging a significant gap in the coverage of the HIS currently implemented in India. With the provision of open, pullable APIs, the NHS also shares the motivations of the IndiaStack to monetise health data.</p>
<p>A key component of the proposed NHS is the Coverage and Claims platform, which the vision document describes as “provid[ing] the building blocks required to implement any large-scale health insurance program, in particular, any government-funded healthcare programs. This platform has the transformative vision of enabling both public and private actors to implement insurance schemes in an automated, data-driven manner through open APIs " (NITI Aayog2018). A post on the iSPIRT website further explains the centrality of this Coverage and Claims platform in enabling a highly personalised medical insurance market in India: “This component will not only bring down the cost of processing a claim but ... increased access to information about an individual’s health and claims history ... will also enable the creation of personalised, sachet-sized insurance policies." These data-driven customised insurance policies are expected to generate “care policies that are not only personalized in nature but that also incentivize good healthcare practices amongst consumers and providers … [and] use of techniques from microeconomics to manage incentives for care providers, and those from behavioural economics to incentivise consumers" (Productnation Network 2019). The Coverage and Claims platform, and especially the Policy (generation) Engine that it will contain, is aimed at intensive financialisation of personal healthcare expenses, and extensive experiments with designing personalised nudges to shape the demand behaviour of consumers.</p>
<p>The imagination of healthcare the NHS demonstrates is one where broadening health insurance coverage is equated to providing equitable healthcare and as a panacea for the public healthcare sector. The first phase of this push towards better healthcare provision is to focus on contextualising the historical socio-economic divide. The next phase is characterised by digitalisation: the introduction of ICT to bridge the socio-economic divide in healthcare provision. In this process, the resulting data divide has been invisibilised in reframing better healthcare as an insurance problem for which data needs to be generated. Each policy innovation is then characterised by further marginalisation of those that were originally identified as underserved. This is a result of increasing repercussions of the data-divide, with access to benefits increasingly being mediated by technology.</p>
<h3><strong>Concluding Remarks</strong></h3>
<blockquote>The idea that any person in India can go to any health service provider/ practitioner, any diagnostic center or any pharmacy and yet be able to access and have fully integrated and always available health records in an electronic format is not only empowering but also the vision for efficient 21st century healthcare delivery.<br />
— Ministry of Health and Family Welfare, Electronic Health Record Standards For India (2013)</blockquote>
<p>The objective of health data collection has evolved over the course of the institution of the HIS in 2011, to the development of the NHPS and National Health Policy in 2017. What began as a solution to measure and address gaps in access and quality in healthcare provisioning through data analysis has morphed into data centralisation and insurance coverage. Shifting goalposts can also be found in the objectives behind introducing digital systems to collect data.</p>
<p>In recent iterations of the healthcare imaginary, such as the IHIP and the NHS, data ownership by the beneficiaries is stressed upon. In the absence of a rights-based framework dictating the use of data, the role of ownership should be interrogated, especially in the context of a prevalent data divide (Tisne 2019). The legitimisation of data capture can be seen in the emergence of opt-in models of consent, data fiduciaries managing consent on the data subject’s behalf, etc. (Zuboff 2019).</p>
<p>This framing forecloses a discussion about the quality and kind of data being used. The push towards datafication needs to be questioned for its re-indexing of categorical meaning away from the complexities of narrative, context and history (Cheney-Lippold 2018). Instead, the proposed solution is one that stores datafied elements within a closed set (reproductive health= [abortion, aids, contraceptive,...vaccination, womb]). While this set may be editable, so new interpretations can be codified, it inherently remains stable, assuming a static relationship between words and meaning. Health is then treated as having an empirically definable meaning, thus losing the dynamism of what the health and wellness discourse could entail.</p>
<p>It has been historically demonstrated in the Indian context that multiple tools and databases for health data management are a barrier to an efficient HIS. However, generating centralised or federated databases without addressing concerns in data flows, quality, uses in existing data structures, and the digital divide across health workers and beneficiaries alike will lead to the amplification of existing exclusions in data and, consequently, service provisioning.</p>
<h3><strong>Acknowledgements</strong></h3>
<p>The author would like to express his gratitude to Sumandro Chattapadhyay and Ambika Tandon for their inputs and editorial work on this contribution. This work was supported by the Big Data for Development Network established by International Development Research Centre (Canada).</p>
<h3><strong>Notes</strong></h3>
<p><strong>[1]</strong> Section 2 (a) of the Clinical Establishments (Registration and Regulation) Act, 2010: A hospital, maternity home, nursing home, dispensary, clinic, sanatorium or institution by whatever name called that offers services, facilities requiring diagnosis, treatment or care for illness, injury, deformity, abnormality or pregnancy in any recognised system of medicine established and administered or maintained by any person or body of persons, whether incorporated or not.</p>
<p><strong>[2]</strong> The National Health Stack, then, is the latest manifestation of the Indian government’s push for a “Digital India.” A key component of Digital India has been e-governance, financial inclusion, and digitisation of transaction services. The nudge towards cashless modes of transaction and delivery, also accelerated by India’s demonetisation drive in November 2016, has led to rapid uptake of digital payment services in particular, and that of the IndiaStack initiative in general. Developed by iSPIRT, IndiaStack (https://indiastack.org/) aspires to transform service delivery by public and private actors alike through its “presence-less, paperless, and cashless” mandate.</p>
<h3><strong>References</strong></h3>
<p>Allan, J and Jane Englebright (2000): “Patient-Centered Documentation,” JONA: The Journal of Nursing Administration, Vol 30, No 2, pp 90–95.</p>
<p>Bach, Peter, Hoangmai Pham, Deborah Schrag, Ramsey Tate and J Lee Hargraves (2004): “Primary Care Physicians Who Treat Blacks and Whites,” New England Journal of Medicine, Vol 351, pp 575–84.</p>
<p>Cheney-Lippold, John (2018): We Are Data: Algorithms and the Making of Our Digital Selves, New Delhi: Sage.</p>
<p>Daly, Jeanette, Buckwalter Kathleen and Meridean Maas (2002): “Written and Computerized Care Plans,” Journal of Gerontological Nursing, Vol 28, No 9, pp 14–23.</p>
<p>EMR Standards Committee (2013): “Recommendations on Electronic Medical Records Standards in India,” Ministry of Health and Family Welfare, Government of India, New Delhi, https://mohfw.gov.in/sites/default/files/24539108839988920051EHR%20Standards-v5%20Apr%202013.pdf.</p>
<p>Federation of Indian Chambers of Commerce and Industry (2015): "A Guiding Framework for OPD and Preventive Health Insurance in India: Supply and Demand Side Analysis," http://ficci.in/spdocument/20678/P&P-helath-insurance.pdf.</p>
<p>Fraser, Hamish, Paul Biondich, Deshendran Moodley, Sharon Choi, Burke Mamlin and Peter Szolovits (2005): “Implementing Electronic Medical Record Systems in Developing Countries,” Journal of Innovation in Health Informatics, Vol 13 No 2, pp 83–95.</p>
<p>Häyrinen, Kristiina, Kaija Saranto and Pirkko Nykänen (2008): “Definition, Structure, Content, Use and Impacts of Electronic Health Records: A Review of the Research Literature,” International Journal of Medical Informatics, Vol 77, No 5, pp 291–304.</p>
<p>Healthcare Information and Management Systems Society (2007): “Electronic Health Records: A Global Perspective,” http://www.providersedge.com/ehdocs/ehr_articles/Electronic_Health_Records-A_Global_Perspective-Exec_Summary.pdf.</p>
<p>Holzemer, William and S B Henry (1992): “Computer-supported Versus Manually-generated Nursing Care Plans: A Comparison of Patient Problems, Nursing Interventions, and AIDS Patient Outcomes,” Computers in Nursing, Vol 10 No 1, pp 19–24.</p>
<p>Jha, Ashish, Catherine DesRoches, Eric Campbell, Karen Donelan, Sowmya Rao, Timothy Ferris, Alexandra Shields, Sarah Rosenbaum and David Blumenthal (2009): "Use of Electronic Health Records in U.S. Hospitals," New England Journal of Medicine, Vol 360 No 16, pp 1628–1638.</p>
<p>Jyoti, Archana (2018): “States Give Clinical Establishment Act Cold Shoulder," Pioneer, https://www.dailypioneer.com/2018/india/states-give-clinical-establishment-act-cold-shoulder.html.</p>
<p>Kandhari, Ruhi (2017): “Why a Backdoor Push Towards eHealth,” Ken, https://the-ken.com/story/why-backdoor-push-towards-ehealth/.</p>
<p>Kovner, Christine, Lynda Schuchman and Catherin Mallard (1997): “The Application of Pen-Based Computer Technology to Home Health Care,” CIN: Computers, Informatics and Nursing, Vol 15, No 5, pp 237–44.</p>
<p>Krishnamurthy, R (2018): “Integrated Health Information Platform for Integrated Disease Surveillance Program,” Training of the Trainer Workshop, World Health Organisation, New Delhi, https://idsp.nic.in/WriteReadData/IHIP/IHIP%20ToT-Overview-Presentation.pdf.</p>
<p>Kuperman, Gilad and Richard Gibson (2003): “Computer Physician Order Entry: Benefits, Costs, and Issues,” Annals of Internal Medicine, Vol 139 No 1, pp 31–9.</p>
<p>Leung, Gabriel, Philip Yu, Irene Wong, Janice Johnston and Keith Tin (2003): “Incentives and Barriers That Influence Clinical Computerization in Hong Kong: A Population-based Physician Survey,” Journal of the American Medical Informatics Association, Vol 10 No 2, pp 201–12.</p>
<p>McGinn Carrie Anna, Sonya Grenier, Julie Duplantie, Nicola Shaw, Claude Sicotte, Luc Mathieu, Yvan Leduc, France Légaré and Marie-Pierre Gagnon (2011): “Comparison of User Groups' Perspectives of Barriers and Facilitators to Implementing Electronic Health Records: A Systematic Review,” BMC Medicine, Vol 9 No 46.</p>
<p>Miklin, Daniel, Sameera Vangara, Alan Delamater and Kenneth Goodman (2019): “Understanding of and Barriers to Electronic Health Record Patient Portal Access in a Culturally Diverse Pediatric Population,” JMIR Medical Informatics, Vol 7, No 2.</p>
<p>Ministry of Finance (2018): “Budget 2018-19: Speech of Arun Jaitley,” New Delhi, https://www.indiabudget.gov.in/ub2018-19/bs/bs.pdf.</p>
<p>Ministry of Health and Family Welfare, Government of India (2008): "4 Years of Transforming India-Healthcare for All," New Delhi. https://mohfw.gov.in/ebook2018/gvtbook.html.</p>
<p>Ministry of Health and Family Welfare, Government of India (2013): “Electronic Health Record Standards For India,” Government of India, New Delhi, https://www.nhp.gov.in/NHPfiles/ehr_2013.pdf.</p>
<p>Ministry of Health and Family Welfare, Government of India (2017): Request for Proposal: Development and Implementation of Integrated Health Information Platform (IHIP), Centre for Health Informatics, National Institute of Health and Family Welfare, New Delhi, https://nhp.gov.in/NHPfiles/IHIP_RFP%20.pdf.</p>
<p>Ministry of Health and Family Welfare, Government of India (2018): “IDSP Segment of Integrated Health Information Platform,” New Delhi, https://idsp.nic.in/index4.php?lang=1&level=0&linkid=454&lid=3977.</p>
<p>National Health Authority (nd): “About Pradhan Mantri Jan Arogya Yojana (PM-JAY) | Ayushmaan Bharat,” https://www.pmjay.gov.in/about-pmjay.</p>
<p>NITI Aayog (2018): “National Health Stack- Strategy and Approach,” NITI Aayog, New Delhi, http://www.niti.gov.in/writereaddata/files/document_publication/NHS-Strategy-and-Approach-Document-for-consultation.pdf.</p>
<p>Organisation for Economic Co-operation and Development (2013): “Strengthening Health Information Infrastructure for Health Care Quality Governance: Good Practices, New Opportunities and Data Privacy Protection Challenges,” OECD Health Policy Studies, Paris, OECD Publishing, https://read.oecd-ilibrary.org/social-issues-migration-health/strengthening-health-information-infrastructure-for-health-care-quality-governance_9789264193505-en.</p>
<p>Poon, Eric, David Blumenthal, Tonushree Jaggi, Melissa Honour, David Bates and Rainu Kaushal (2004): “Overcoming Barriers to Adopting and Implementing Computerized Physician Order Entry Systems in U.S. Hospitals,” Health Affairs, Vol 23 No 4, pp 184–90.</p>
<p>Productnation Network (2019): “India’s Health Leapfrog–Towards A Holistic Healthcare Ecosystem,” iSpirt, https://pn.ispirt.in/towards-a-holistic-healthcare-ecosystem/.</p>
<p>Rathi, Aayush and Ambika Tandon (2019): “Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?” EPW Engage, https://www.epw.in/engage/article/data-infrastructures-inequities-why-does-reproductive-health-surveillance-india-need-urgent-attention.</p>
<p>Sequist, Thomas, Theresa Cullen, Howard Hays, Maile Taualii, Steven Simon, and David Bates (2007): “Implementation and Use of an Electronic Health Record Within the Indian Health Service,” Journal of the American Medical Informatics Association, Vol 14, No 2, pp 191–97.</p>
<p>World Bank (nd): Physicians (per 1,000 people) | Data, https://data.worldbank.org/indicator/SH.MED.PHYS.ZS.</p>
<p>Tierney, William et al. (2010): “Experience Implementing Electronic Health Records in Three East African Countries,” Studies in Health Technology and Informatics, Vol 160, No 1, pp 371–75.</p>
<p>Tisne, Martin (2018): “It’s Time for a Bill of Data Rights,” MIT Technology Review, https://www.technologyreview.com/s/612588/its-time-for-a-bill-of-data-rights/.</p>
<p>World Health Organization (2016): “Global Diffusion of eHealth: Making Universal Health Coverage Achievable,” https://apps.who.int/iris/bitstream/handle/10665/252529/9789241511780-eng.pdf;jsessionid=9DD5F8603C67EEF35549799B928F3541?sequence=1.</p>
<p>Zuboff, Soshana (2019): The Age of Surveillance Capitalism, New York: PublicAffairs.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/raw/is-indias-digital-health-system-foolproof'>http://editors.cis-india.org/raw/is-indias-digital-health-system-foolproof</a>
</p>
No publisheraayushEHRBig DataBig Data for DevelopmentResearchBD4DHealthcareResearchers at Work2019-12-30T17:58:00ZBlog EntryFuture Value of Data
http://editors.cis-india.org/internet-governance/news/future-value-of-data
<b>Carnegie India with support of Facebook organized a workshop in Bengaluru on January 10, 2018. Sunil Abraham participated in the workshop.</b>
<p style="text-align: justify; ">The event focused on the political economy of reform in India, foreign and security policy, and the role of innovation and technology in India's internal transformation and international relations.</p>
<p style="text-align: justify; ">Core aims of the workshop included:</p>
<ul>
<li>Share and debate views on what changes we expect in the value of data over next decade.</li>
<li>Challenge and explore the underlying drivers of change across broad arena.</li>
<li>Debate the regional and global perspectives and highlight unique issues of greatest impact.</li>
<li>Build an informed collective view on the topic for all to use going forward.</li>
</ul>
<p>For more details on Future of Value Data, <a class="external-link" href="https://www.futureagenda.org/news/future-value-of-data">click here</a></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/future-value-of-data'>http://editors.cis-india.org/internet-governance/news/future-value-of-data</a>
</p>
No publisherAdminInternet GovernanceBig Data2018-01-17T00:32:50ZNews ItemNASA International Open Data Challenge 2015
http://editors.cis-india.org/openness/events/nasa-international-open-data-challenge-2015
<b>As part of the initial NASA Open Government 2.0 plan, the NASA International Open Data challenge brings together the FOSS community, citizen scientists, open data practitioners , open hardware enthusiasts and students for collaborative problem solving with the goal of producing relevant open-source solutions to address global needs applicable to both life on Earth and life in Space.</b>
<p style="text-align: justify; ">On April 11 and 12, 2015 2015, the event will be organized by the Centre for Internet and Society in collaboration with mentors from Team Indus, one of India's leading Space Technology Start-Ups. The event will start off with the following keynote and workshops at 9am on Saturday, April 11th, 2015:</p>
<div style="text-align: justify; "><b>Pre-Hackathon Workshop: 9 a.m., Saturday, April 11, 2015</b></div>
<div style="text-align: justify; ">IBM Blue Mix Team + OpenCube Labs</div>
<div style="text-align: justify; ">(Big Data Analytics using Cloud Services like Bluemix/Heroku, with node.js implementation and Android APIs)</div>
<div style="text-align: justify; "></div>
<div style="text-align: justify; ">
<div><b>Keynote: Amar Sharma, 12 p.m., April 11, 2015</b></div>
<div>Amar is credited as being the youngest and first Indian amateur astronomer to have an Asteroid named after him in 2014 at the age of 29. <b>(380607 Sharma)</b> He will talk about CCD Astro Imaging and his travails and journey as a self-made astronomer and comet hunter.</div>
<div></div>
<div>We will then break off into teams to commence the hackathon that will end on Sunday,April 12, 2015 at 6pm, after which teams will upload and present their solutions for Local and Global Nominations.</div>
<div></div>
<div>Registration is free and you are required to confirm participation at the below link:</div>
<div><a href="https://2015.spaceappschallenge.org/location/bangalore/">https://2015.spaceappschallenge.org/location/bangalore/</a></div>
</div>
<div style="text-align: justify; "></div>
<div style="text-align: justify; ">Participants are requested to bring their own laptop/computing devices.</div>
<hr />
<p> </p>
<div style="text-align: justify; ">Please see last year's event's focus on Open Science and Big data, and the various Open Data solutions developed at CIS, to get an idea of what the event is about:</div>
<div style="text-align: justify; "><a href="https://2014.spaceappschallenge.org/location/bangalore/">https://2014.spaceappschallenge.org/location/bangalore/</a> This year, we will have a workshop on Big Data Analytics conducted by IBM BlueMix Labs followed by Heroku implementation and Android Programming by friends of CIS from OpenCubeLabs, that will follow a very special Keynote speaker who is first amateur astronomer to have an asteroid named after him, to join the likes of Ramanujan and Vikram Sarabhai.</div>
<p>
For more details visit <a href='http://editors.cis-india.org/openness/events/nasa-international-open-data-challenge-2015'>http://editors.cis-india.org/openness/events/nasa-international-open-data-challenge-2015</a>
</p>
No publishersharathOpen DataEventBig DataOpenness2015-04-27T01:08:27ZEventReport on Understanding Aadhaar and its New Challenges
http://editors.cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges
<b>The Trans-disciplinary Research Cluster on Sustainability Studies at Jawaharlal Nehru University collaborated with the Centre for Internet and Society, and other individuals and organisations to organise a two day workshop on “Understanding Aadhaar and its New Challenges” at the Centre for Studies in Science Policy, JNU on May 26 and 27, 2016. The objective of the workshop was to bring together experts from various fields, who have been rigorously following the developments in the Unique Identification (UID) Project and align their perspectives and develop a shared understanding of the status of the UID Project and its impact. Through this exercise, it was also sought to develop a plan of action to address the welfare exclusion issues that have arisen due to implementation of the UID Project.</b>
<p> </p>
<h4>Report: <a href="http://editors.cis-india.org/internet-governance/files/report-on-understanding-aadhaar-and-its-new-challenges/at_download/file">Download</a> (PDF)</h4>
<hr />
<p style="text-align: justify;">This Report is a compilation of the observations made by participants at the workshop relating to myriad issues under the UID Project and various strategies that could be pursued to address these issues. In this Report we have classified the observations and discussions into following themes:</p>
<p><strong>1.</strong> <a href="#1">Brief Background of the UID Project</a></p>
<p><strong>2.</strong> <a href="#2">Legal Status of the UIDAI Project</a></p>
<ul>
<li><a href="#21">Procedural issues with passage of the Act</a></li>
<li><a href="#22">Status of related litigation</a></li></ul>
<p><strong>3.</strong> <a href="#3">National Identity Projects in Other Jurisdictions</a></p>
<ul>
<li><a href="#31">Pakistan</a></li>
<li><a href="#32">United Kingdom</a></li>
<li><a href="#33">Estonia</a></li>
<li><a href="#34">France</a></li>
<li><a href="#35">Argentina</a></li></ul>
<p><strong>4.</strong> <a href="#4">Technologies of Identification and Authentication</a></p>
<ul>
<li><a href="#41">Use of Biometric Information for Identification and Authentication</a></li>
<li><a href="#42">Architectures of Identification</a></li>
<li><a href="#43">Security Infrastructure of CIDR</a></li></ul>
<p><strong>5.</strong> <a href="#5">Aadhaar for Welfare?</a></p>
<ul>
<li><a href="#51">Social Welfare: Modes of Access and Exclusion</a></li>
<li><a href="#52">Financial Inclusion and Direct Benefits Transfer</a></li></ul>
<p><strong>6.</strong> <a href="#6">Surveillance and UIDAI</a></p>
<p><strong>7.</strong> <a href="#7">Strategies for Future Action</a></p>
<p><strong>Annexure A</strong> <a href="#AA">Workshop Agenda</a></p>
<p><strong>Annexure B</strong> <a href="#AB">Workshop Participants</a></p>
<hr />
<h3 id="1" style="text-align: justify;"><strong>1. Brief Background of the UID Project</strong></h3>
<p style="text-align: justify;">In the year 2009, the UIDAI was established and the UID project was conceived by the Planning Commission under the UPA government to provide unique identification for each resident in India and to be used for delivery of welfare government services in an efficient and transparent manner, along with using it as a tool to monitor government schemes. The objective of the scheme has been to issue a unique identification number by the Unique Identification Authority of India, which can be authenticated and verified online. It was conceptualized and implemented as a platform to facilitate identification and avoid fake identity issues and delivery of government benefits based on the demographic and biometric data available with the Authority.</p>
<p style="text-align: justify;">The Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act, 2016 (the “<strong>Act</strong>”) was passed as a money bill on March 16, 2016 and was notified in the gazette March 25, 2016 upon receiving the assent of the President. However, the enforceability date has not been mentioned due to which the bill has not come into force.</p>
<p style="text-align: justify;">The Act provides that the Aadhaar number can be used to validate a person’s identity, but it cannot be used as a proof of citizenship. Also, the government can make it mandatory for a person to authenticate her/his identity using Aadhaar number before receiving any government subsidy, benefit, or service. At the time of enrolment, the enrolling agency is required to provide notice to the individual regarding how the information will be used, the type of entities the information will be shared with and their right to access their information. Consent of an individual would be obtained for using his/her identity information during enrolment as well as authentication, and would be informed of the nature of information that may be shared. The Act clearly lays that the identity information of a resident shall not be sued for any purpose other than specified at the time of authentication and disclosure of information can be made only pursuant to an order of a court not inferior to that of a District Judge and/or disclosure made in the interest of national security.</p>
<h3 id="2" style="text-align: justify;"><strong>2. Legal Status of the UIDAI Project</strong></h3>
<p style="text-align: justify;">In this section, we have summarised the discussions on the procedural issues with the passage of the Act. The participants had criticised the passage of the Act as a money bill in the Parliament. The participants also assessed the litigation pending in the Supreme Court of India that would be affected by this law. These discussions took place in the session titled, ‘Current Status of Aadhaar’ and have been summarised below.</p>
<h3 id="21" style="text-align: justify;">Procedural Issues with Passage of the Act</h3>
<p style="text-align: justify;">The participants contested the introduction of the Act in the form of a money bill. The rationale behind this was explained at the session and is briefly explained here. Article 110 (1) of the Constitution of India defines a money bill as one containing provisions only regarding the matters enumerated or any matters incidental to the following: a) imposition, regulation and abolition of any tax, b) borrowing or other financial obligations of the Government of India, c) custody, withdrawal from or payment into the Consolidated Fund of India (CFI) or Contingent Fund of India, d) appropriation of money out of CFI, e) expenditure charged on the CFI or f) receipt or custody or audit of money into CFI or public account of India. The Act makes references to benefits, subsidies and services which are funded by the Consolidated Fund of India (CFI), however the main objectives of the Act is to create a right to obtain a unique identification number and provide for a statutory mechanism to regulate this process. The Act only establishes an identification mechanism which facilitates distribution of benefits and subsidies funded by the CFI and this identification mechanism (Aadhaar number) does not give it the character of a money bill. Further, money bills can be introduced only in the Lok Sabha, and the Rajya Sabha cannot make amendments to such bills passed by the Lok Sabha. The Rajya Sabha can suggest amendments, but it is the Lok Sabha’s choice to accept or reject them. This leaves the Rajya Sabha with no effective role to play in the passage of the bill.</p>
<p style="text-align: justify;">The participants also briefly examined the writ petition that has been filed by former Union minister Jairam Ramesh challenging the constitutionality and legality of the treatment of this Act as a money bill which has raised the question of judiciary’s power to review the decisions of the speaker. Article 122 of the Constitution of India provides that this power of judicial review can be exercised to look into procedural irregularities. The question remains whether the Supreme Court will rule that it can determine the constitutionality of the decision made by the speaker relating to the manner in which the Act was introduced in the Lok Sabha. A few participants mentioned that similar circumstances had arisen in the case of Mohd. Saeed Siddiqui v. State of U.P. <a href="#ftn1">[1]</a>.</p>
<p style="text-align: justify;">where the Supreme Court refused to interfere with the decision of the Uttar Pradesh legislative assembly speaker certifying an amendment bill to increase the tenure of the Lokayukta as a money bill, despite the fact that the bill amended the Uttar Pradesh Lokayukta and Up-Lokayuktas Act, 1975, which was passed as an ordinary bill by both houses. The Court in this case held that the decision of the speaker was final and that the proceedings of the legislature being important legislative privilege could not be inquired into by courts. The Court added, “the question whether a bill is a money bill or not can be raised only in the state legislative assembly by a member thereof when the bill is pending in the state legislature and before it becomes an Act.”</p>
<p style="text-align: justify;">However, it is necessary to carve a distinction between Rajya Sabha and State Legislature. Unlike the State Legislature, constitution of Rajya Sabha is not optional therefore significance of the two bodies in the parliamentary process cannot be considered the same. Participants also made another significant observation about a similar bill on the UID project (National Identification Authority of India (NIDAI) Bill) that was introduced before by the UPA government in 2010 and was deemed unacceptable by the standing committee on finance, headed by Yashwant Sinha. This bill was subsequently withdrawn.</p>
<h3 id="22" style="text-align: justify;">Status of Related Litigation</h3>
<p style="text-align: justify;">A panellist in this session briefly summarised all the litigation that was related to or would be affected by the Act. The panellist also highlighted several Supreme Court orders in the case of <em>KS Puttuswamy v. Union of India</em> <a href="#ftn2">[2]</a> which limited the use of Aadhaar. We have reproduced the presentation below.</p>
<ul>
<li style="text-align: justify;"><em>KS Puttuswamy v. Union of India</em> - This petition was filed in 2012 with primary concern about providing Aadhaar numbers to illegal immigrants in India. It was contended that this could not be done without a law establishing the UIDAI and amendment to the Citizenship laws. The petitioner raised concerns about privacy and fallibility of biometrics.</li>
<li style="text-align: justify;"> Sudhir Vombatkere & Bezwada Wilson <a href="#ftn3">[3]</a> - This petition was filed in 2013 on grounds of infringement of right to privacy guaranteed under Article 21 of the Constitution of India and the security threat on account of data convergence.</li>
<li style="text-align: justify;">Aruna Roy & Nikhil Dey <a href="#ftn4">[4]</a> - This petition was filed in 2013 on the grounds of large scale exclusion of people from access to basic welfare services caused by UID. After their petition, no. of intervention applications were filed. These were the following:</li>
<li style="text-align: justify;">Col. Mathew Thomas <a href="#ftn5">[5]</a> - This petition was filed on the grounds of threat to national security posed by the UID project particularly in relation to arrangements for data sharing with foreign companies (with links to foreign intelligence agencies).</li>
<li style="text-align: justify;">Nagrik Chetna Manch <a href="#ftn6">[6]</a> - This petition was filed in 2013 and led by Dr. Anupam Saraph on the grounds that the UID project was detrimental to financial service regulation and financial <em>inclusion.</em></li>
<li style="text-align: justify;">S. Raju <a href="#ftn7">[7] </a> - This petition was filed on the grounds that the UID project had implications on the federal structure of the State and was detrimental to financial inclusion.</li>
<li style="text-align: justify;"><em>Beghar Foundation</em> - This petition was filed in 2013 in the Delhi High Court on the grounds invasion of privacy and exclusion specifically in relation to the homeless. It subsequently joined the petition filed by Aruna Roy and Nikhil Dey as an intervener.</li>
<li style="text-align: justify;">Vickram Crishna – This petition was originally filed in the Bombay High Court in 2013 on the grounds of surveillance and invasion of privacy. It was later transferred to the Supreme Court.</li>
<li style="text-align: justify;">Somasekhar – This petition was filed on the grounds of procedural unreasonableness of the UID project and also exclusion & privacy. The petitioner later intervened in the petition filed by Aruna Roy and Nikhil Dey in 2013.</li>
<li style="text-align: justify;">Rajeev Chandrashekhar– This petition was filed on the ground of lack of legal sanction for the UID project. He later intervened in the petition filed by Aruna Roy and Nikhil Dey in 2013. His position has changed now.</li>
<li style="text-align: justify;">Further, a petition was filed by Mr. Jairam Ramesh initially challenging the passage of the Act as a money bill but subsequently, it has been amended to include issues of violation of right to privacy and exclusion of the poor and has advocated for five amendments that were suggested to the Aadhaar Bill by the Rajya Sabha.</li></ul>
<h3 id="23" style="text-align: justify;">Relevant Orders of the Supreme Court</h3>
<p>There are six orders of the Supreme Court which are noteworthy.</p>
<ul>
<li style="text-align: justify;">Order of Sept. 23, 2013 - The Supreme court directed that: 1) no person shall suffer for not having an aadhaar number despite the fact that a circular by an authority makes it mandatory; 2) it should be checked if a person applying for aadhaar number voluntarily is entitled to it under the law; and 3) precaution should be taken that it is not be issued to illegal immigrants.</li>
<li style="text-align: justify;">Order of 26th November, 2013 – Applications were filed by UIDAI, Ministry of Petroleum & Natural Gas, Govt of India, Indian Oil Corporation, BPCL and HPCL for modifying the September 23rd order and sought permission from the Supreme Court to make aadhaar number mandatory. The Supreme Court held that the order of September 23rd would continue to be effective.</li>
<li style="text-align: justify;">Order of 24th March, 2014 – This order was passed by the Supreme Court in a special leave petition filed in the case of <em>UIDAI v CBI</em> <a href="#ftn8">[8] </a> wherein UIDAI was asked to UIDAI to share biometric information of all residents of a particular place in Goa to facilitate a criminal investigation involving charges of rape and sexual assault. The Supreme Court restrained UIDAI from transferring any biometric information of an individual without to any other agency without his consent in writing. The Supreme Court also directed all the authorities to modify their forms/circulars/likes so as to not make aadhaar number mandatory.</li>
<li style="text-align: justify;">Order of 16th March, 2015 - The SC took notice of widespread violations of the order passed on September 23rd, 2013 and directed the Centre and the states to adhere to these orders to not make aadhaar compulsory.</li>
<li style="text-align: justify;">Orders of August 11, 2015 – In the first order, the Central Government was directed to publicise the fact that aadhaar was voluntary. The Supreme Court further held that provision of benefits due to a citizen of India would not be made conditional upon obtaining an aadhaar number and restricted the use of aadhaar to the PDS Scheme and in particular for the purpose of distribution of foodgrains, etc. and cooking fuel, such as kerosene and the LPG Distribution Scheme. The Supreme Court also held that information of an individual that was collected in order to issue an aadhaar number would not be used for any purpose except when directed by the Court for criminal investigations. Separately, the status of fundamental right to privacy was contested and accordingly the Supreme Court directed that the issue be taken up before the Chief Justice of India.</li>
<li style="text-align: justify;">Orders of October 16, 2015 – The Union of India, the states of Gujarat, Maharashtra, Himachal Pradesh and Rajasthan, and authorities including SEBI, TRAI, CBDT, IRDA , RBI applied for a hearing before the Constitution Bench for modification of the order passed by the Supreme Court on August 11 and allow use of aadhaar number schemes like The Mahatma Gandhi National Rural Employment Guarantee Scheme MGNREGS), National Social Assistance Programme (Old Age Pensions, Widow Pensions, Disability Pensions) Prime Minister's Jan Dhan Yojana (PMJDY) and Employees' Providend Fund Organisation (EPFO). The Bench allowed the use of aadhaar number for these schemes but stressed upon the need to keep aadhaar scheme voluntary until the matter was finally decided.</li></ul>
<p style="text-align: justify;">Status of these orders<br />The participants discussed the possible impact of the law on the operation of these orders. A participant pointed out that matters in the Supreme Court had not become infructuous because fundamental issues that were being heard in the Supreme Court had not been resolved by the passage of the Act. Several participants believed that the aforementioned orders were effective because the law had not come into force. Therefore, aadhaar number could only be used for purposes specified by the Supreme Court and it could not be made mandatory. Participants also highlighted that when the Act was implemented, it would not nullify the orders of the Supreme Court unless Union of India asked the Supreme Court for it specifically and the Supreme Court sanctioned that.</p>
<h3 id="3" style="text-align: justify;"><strong>3. National Identity Projects in Other Jurisdictions</strong></h3>
<p style="text-align: justify;">A panellist had provided a brief overview of similar programs on identification that have been launched in other jurisdictions including Pakistan, United Kingdom, France, Estonia and Argentina in the recent past in the session titled ‘Aadhaar - International Dimensions’. This presentation mainly sought to assess the incentives that drove the governments in these jurisdictions to formulate these projects, mandatory nature of their adoption and their popularity. The Report has reproduced the presentation here.</p>
<h3 id="31" style="text-align: justify;">Pakistan</h3>
<p style="text-align: justify;">The Second Amendment to the Constitution of Pakistan in 2000 established the National Database and Regulation Authority in the country, which regulates government databases and statistically manages the sensitive registration database of the citizens of Pakistan. It is also responsible for issuing national identity cards to the citizens of Pakistan. Although the card is not legally compulsory for a Pakistani citizen, it is mandatory for:</p>
<ul>
<li>Voting</li>
<li>Obtaining a passport</li>
<li>Purchasing vehicles and land</li>
<li>Obtaining a driver licence</li>
<li>Purchasing a plane or train ticket</li>
<li>Obtaining a mobile phone SIM card</li>
<li>Obtaining electricity, gas, and water</li>
<li>Securing admission to college and other post-graduate institutes</li>
<li>Conducting major financial transactions</li></ul>
<p style="text-align: justify;">Therefore, it is pretty much necessary for basic civic life in the country. In 2012, NADRA introduced the Smart National Identity Card, an electronic identity card, which implements 36 security features. The following information can be found on the card and subsequently the central database: Legal Name, Gender (male, female, or transgender), Father's name (Husband's name for married females), Identification Mark, Date of Birth, National Identity Card Number, Family Tree ID Number, Current Address, Permanent Address, Date of Issue, Date of Expiry, Signature, Photo, and Fingerprint (Thumbprint). NADRA also records the applicant's religion, but this is not noted on the card itself. (This system has not been removed yet and is still operational in Pakistan.)</p>
<h3 id="32" style="text-align: justify;">United Kingdom</h3>
<p style="text-align: justify;">The Identity Cards Act was introduced in the wake of the terrorist attacks on 11th September, 2001, amidst rising concerns about identity theft and the misuse of public services. The card was to be used to obtain social security services, but the ability to properly identify a person to their true identity was central to the proposal, with wider implications for prevention of crime and terrorism. The cards were linked to a central database (the National Identity Register), which would store information about all of the holders of the cards. The concerns raised by human rights lawyers, activists, security professionals and IT experts, as well as politicians were not to do with the cards as much as with the NIR. The Act specified 50 categories of information that the NIR could hold, including up to 10 fingerprints, digitised facial scan and iris scan, current and past UK and overseas places of residence of all residents of the UK throughout their lives. The central database was purported to be a prime target for cyber attacks, and was also said to be a violation of the right to privacy of UK citizens. The Act was passed by the Labour Government in 2006, and repealed by the Conservative-Liberal Democrat Coalition Government as part of their measures to “reverse the substantial erosion of civil liberties under the Labour Government and roll back state intrusion.”</p>
<h3 id="33" style="text-align: justify;">Estonia</h3>
<p style="text-align: justify;">The Estonian i-card is a smart card issued to Estonian citizens by the Police and Border Guard Board. All Estonian citizens and permanent residents are legally obliged to possess this card from the age of 15. The card stores data such as the user's full name, gender, national identification number, and cryptographic keys and public key certificates. The cryptographic signature in the card is legally equivalent to a manual signature, since 15 December 2000. The following are a few examples of what the card is used for:</p>
<ul>
<li>As a national ID card for legal travel within the EU for Estonian citizens</li>
<li>As the national health insurance card</li>
<li>As proof of identification when logging into bank accounts from a home computer</li>
<li>For digital signatures</li>
<li>For i-voting</li>
<li>For accessing government databases to check one’s medical records, file taxes, etc.</li>
<li>For picking up e-Prescriptions</li>
<li>(This system is also operational in the country and has not been removed)</li></ul>
<h3 id="34" style="text-align: justify;">France</h3>
<p style="text-align: justify;">The biometric ID card was to include a compulsory chip containing personal information, such as fingerprints, a photograph, home address, height, and eye colour. A second, optional chip was to be implemented for online authentication and electronic signatures, to be used for e-government services and e-commerce. The law was passed with the purpose of combating “identity fraud”. It was referred to the Constitutional Council by more than 200 members of the French Parliament, who challenged the compatibility of the bill with the citizens’ fundamental rights, including the right to privacy and the presumption of innocence. The Council struck down the law, citing the issue of proportionality. “Regarding the nature of the recorded data, the range of the treatment, the technical characteristics and conditions of the consultation, the provisions of article 5 touch the right to privacy in a way that cannot be considered as proportional to the meant purpose”.</p>
<h3 id="35" style="text-align: justify;">Argentina</h3>
<p style="text-align: justify;">Documento Nacional de Identidad or DNI (which means National Identity Document) is the main identity document for Argentine citizens, as well as temporary or permanent resident aliens. It is issued at a person's birth, and updated at 8 and 14 years of age simultaneously in one format: a card (DNI tarjeta); it's valid if identification is required, and is required for voting. The front side of the card states the name, sex, nationality, specimen issue, date of birth, date of issue, date of expiry, and transaction number along with the DNI number and portrait and signature of the card's bearer. The back side of the card shows the address of the card's bearer along with their right thumb fingerprint. The front side of the DNI also shows a barcode while the back shows machine-readable information. The DNI is a valid travel document for entering Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela. (System still operational in the country)</p>
<h3 id="4" style="text-align: justify;"><strong>4. Technologies of Identification and Authentication</strong></h3>
<p style="text-align: justify;">The panel in the session titled ‘Aadhaar: Science, Technology, and Security’ explained the technical aspects of use of biometrics and privacy concerns, technology architecture for identification and inadequacy of infrastructure for information security. In this section, we have summarised the presentation and the ensuing discussions on these issues.</p>
<h3 id="41" style="text-align: justify;">Use of Biometric Information for Identification and Authentication</h3>
<p style="text-align: justify;">The panelists explained with examples that identification and authentication were different things. Identity provides an answer to the question “who are you?” while authentication is a challenge-response process that provides a proof of the claim of identity. Common examples of identity are User ID (Login ID), cryptographic public keys and ATM or Smart cards while common authenticators are passwords (including OTPs), PINs and cryptographic private keys. Identity is public information but an authenticator must be private and known only to the user. Authentication must necessarily be a conscious process and active participation by the user is a must. It should also always be possible to revoke an authenticator. After providing this understanding of the two processes the panellist then explained if biometric information could be used for identification or authentication under the UID Project. Biometric information is clearly public information and it is questionable if it can be revoked. Therefore it should never be used for authentication, but only for identity verification. There is a possibility of authentication by fingerprints under the UID Project, without conscious participation of the user. One could trace the fingerprints of an individual from any place the individual has been in contact with. Therefore, authentication must certainly be done by other means. The panellist pointed out that there were five kinds of authentication under the UID Project, out of which two-factor authentication and one time password were considered suitable but use of biometric information and demographic information was extremely threatening and must be withdrawn.</p>
<h3 id="42" style="text-align: justify;">Architectures of Identification</h3>
<p style="text-align: justify;">The panelists explained the architecture of the UID Project that has been designed for identification purposes, highlighted its limitations and suggested alternatives. His explanations are reproduced below.</p>
<p style="text-align: justify;">Under the UID Project, there is a centralised means of identification i.e. the aadhaar number and biometric information stored in one place, Central Identification Data Repository (CIDR). It is better to have multiple means of identification than one (as contemplated under the UID Project) for preservation of our civil liberties. The question is what the available alternatives are. Web of trust is a way for operationalizing distributed identification but the challenge is how one brings people from all social levels to participate in it. There is a need for registrars who will sign keys and public databases for this purpose.</p>
<p style="text-align: justify;">The aadhaar number functions as a common index and facilitates correlation of data across Government databases. While this is tremendously attractive it raises several privacy concerns as more and more information relating to an individual is available to others and is likely to be abused.</p>
<p style="text-align: justify;">The aadhaar number is available in human readable form. This raises the risk of identification without consent and unauthorised profiling. It cannot be revoked. Potential for damage in case of identity theft increases manifold.</p>
<p style="text-align: justify;">Under the UID Project, for the purpose of information security, Authentication User Agencies (“<strong>AUA</strong>”) are required to use local identifiers instead of aadhaar numbers but they are also required to map these local identifiers to the aadhaar numbers. Aadhaar numbers are not cryptographically secured; in fact they are publicly available. Hence this exercise for securing information is useless. An alternative would be to issue different identifiers for different domains and cryptographically embed a “master identifier” (in this case, equivalent of aadhaar number) into each local identifier.</p>
<p style="text-align: justify;">All field devices (for example POS machines) should be registered and must communicate directly with UIDAI. In fact, UIDAI must verify the authenticity (tamper proof) of the field device during run time and a UIDAI approved authenticity certificate must be issued for field devices. This certificate must be made available to users on demand. Further, the security and privacy frameworks within which AUAs work must be appropriately defined by legal and technical means.</p>
<h3 id="43" style="text-align: justify;">Security Infrastructure of CIDR</h3>
<p style="text-align: justify;">The panelists also enumerated the security features of the UID Project and highlighted the flaws in these features. These have been summarised below.</p>
<p>The security and privacy infrastructure of UIDAI has the following main features:</p>
<ul>
<li>2048 bit PKI encryption of biometric data in transit</li>
<li>End-to-end encryption from enrolment/POS to CIDR</li>
<li>HMAC based tamper detection of PID blocks</li>
<li>Registration and authentication of AUAs</li>
<li>Within CIDR only a SHA 1 Hash of Aadhaar number is stored</li>
<li>Audit trails are stored SHA 1 encrypted. Tamper detection?</li>
<li>Only hashes of passwords and PINs are stored. (biometric data stored in original form though!)</li>
<li>Authentication requests have unique session keys and HMAC</li>
<li>Resident data stored using 100 way sharding (vertical partitioning). First two digits of Aadhaar number as shard keys</li>
<li>All enrolment and update requests link to partitioned databases using Ref IDs (coded indices)</li>
<li>All accesses through a hardware security module</li>
<li>All analytics carried out on anonymised data</li></ul>
<p style="text-align: justify;">The panellists pointed out the concerns about information security on account of design flaws, lack of procedural safeguards, openness of the system and too much trust imposed on multiple players. All symmetric and private keys and hashes are stored somewhere within UIDAI. This indicates that trust is implicitly assumed which is a glaring design flaw. There is no well-defined approval procedure for data inspection, whether it is for the purpose of investigation or for data analytics. There is a likelihood of system hacks, insider leaks, and tampering of authentication records and audit trails. The ensuing discussions highlighted that the UIDAI had admitted to these security risks. The enrolment agencies and the enrolment devices cannot be trusted. AUAs cannot be trusted with biometric and demographic data; neither can they be trusted with sensitive user data of private nature. There is a need for an independent third party auditor for distributed key management, auditing and approving UIDAI programs, including those for data inspection and analytics, whitebox cryptographic compilation of critical parts of the UIDAI programs, issue of cryptographic keys to UIDAI programs for functional encryption, challenge-response for run-time authentication and certification of UIDAI programs. The panellist recommended that there was a need to to put a suitable legal framework to execute this.</p>
<p style="text-align: justify;">The participants also discussed that information infrastructure must not be made of proprietary software (possibility for backdoors for US) and there must be a third party audit with a non-negotiable clause for public audit.</p>
<h3 id="5" style="text-align: justify;"><strong>5. Aadhaar for Welfare?</strong></h3>
<p style="text-align: justify;">The Report has summarised the discussions that took place in the sessions on ‘Direct Benefits Transfers’ and ‘Aadhaar: Broad Issues - II’ where the panellists critically analysed the claims of benefits and inclusion of Aadhaar made by the government in light of the ground realities in states where Aadhaar has been adopted for social welfare schemes.</p>
<h3 id="51" style="text-align: justify;">Social Welfare: Modes of Access and Exclusion</h3>
<p style="text-align: justify;">Under the Act, a person may be required to authenticate or give proof of the aadhaar number in order to receive subsidy from the government (Section 7). A person is required to punch their fingerprints on POS machines in order to receive their entitlement under the social welfare schemes such as LPG and PDS. It was pointed out in the discussions that various states including Rajasthan and Delhi had witnessed fingerprint errors while doling out benefits at ration shops under the PDS scheme. People have failed to receive their entitled benefits because of these fingerprint errors thus resulting in exclusion of beneficiaries <a href="#ftn9">[9]</a>. A panellist pointed out that in Rajasthan, dysfunctional biometrics had led to further corruption in ration shops. Ration shop owners often lied to the beneficiaries about functioning of the biometric machines (POS Machines) and kept the ration for sale in the market therefore making a lot of money at the expense of uninformed beneficiaries and depriving them of their entitlements.</p>
<p style="text-align: justify;">Another participant organisation also pointed out similar circumstances in the ration shops in Patparganj and New Delhi constituencies. Here, the dealers had maintained the records of beneficiaries who had been categorized as follows: beneficiaries whose biometrics did not match, beneficiaries whose biometrics matched and entitlements were provided, beneficiaries who never visited the ration shop. It had been observed that there were no entries in the category of beneficiaries whose biometrics did not match however, the beneficiaries had a different story to tell. They complained that their biometrics did not match despite trying several times and there was no mechanism for a manual override. Consequently, they had not been able to receive any entitlements for months. The discussions also pointed out that the food authorities had placed complete reliance on authenticity of the POS machines and claim that this system would weed out families who were not entitled to the benefits. The MIS was also running technical glitches as a result there was a problem with registering information about these transactions hence, no records had been created with the State authority about these problems. A participant also discussed the plight of 30,000 widows in Delhi, who were entitled to pension and used to collect their entitlement from post offices, faced exclusion due to transition problems under the Jan Dhan Yojana (after the Jandhan was launched the money was transferred to their bank accounts in order to resolve the problem of misappropriation of money at the hands of post office officials). These widows were asked to open bank accounts to receive their entitlements and those who did not open these accounts and did not inform the post office were considered bogus.</p>
<p style="text-align: justify;">In the discussions, the participants also noted that this unreliability of fingerprints as a means of authentication of an individual’s identity was highlighted at the meeting of Empowered Group of Ministers in 2011 by J Dsouza, a biometrics scientist. He used his wife’s fingerprints to demonstrate that fingerprints may change overtime and in such an event, one would not be able to use the POS machine anymore as the machine would continue to identify the impressions collected initially.</p>
<p style="text-align: justify;">The participants who had been working in the field had contributed to the discussions by busting the myth that the UID Project helped to identify who was poor and resolve the problem of exclusion due to leakages in the social welfare programs. These discussions have been summarised below.</p>
<ul>
<li style="text-align: justify;">It is important to understand that the UID Project is merely an identification and authentication system. It only helps in verifying if an individual is entitled to benefits under a social security scheme. It does not ensure plugging of leakages and reducing corruption in social security schemes as has been claimed by the Government. The reduction in leakage of PDS, for instance, should be attributed to digitization and not UID. The Government claims, that it has saved INR 15000 crore in provision of LPG on identification of 3.34 crore inactive accounts on account of the UID Project. This is untrue because the accounts were weeded by using mechanisms completely unrelated to the UID Project. Consequently, the savings on account of UID are only of INR 120 crore and not 15000 crore.</li>
<li style="text-align: justify;">The UID Project has resulted in exclusion of people either because they do not have an aadhaar number, or they have a wrong identification, or there are errors of classification or wilful misclassification. About 99.7% people who were given aadhaar numbers already had an identification document. In fact, during enrolment a person is required to produce one of 14 identification documents listed under the law in order to get an aadhaar number which makes it very difficult for a person with no identity to become entitled to a social welfare scheme.</li></ul>
<p style="text-align: justify;">A participant condemned the Government’s claim that the UID Project had helped in removing fake, bogus and duplicate cards and said that these terms could not be used synonymously and the authorities had no clarity about the difference between the meanings of these terms. The UID Project had only helped in removal of duplicate cards but had not helped in combating the use of fake and bogus cards.</p>
<h3 id="52" style="text-align: justify;">Financial Inclusion and Direct Benefits Transfer</h3>
<p style="text-align: justify;">The participants also engaged in the discussions about the impact of the UID project on financial inclusion in India in the sessions titled ‘Aadhaar: Broad Issues - I & II’. We have summarised these discussions below.</p>
<p style="text-align: justify;">The UID Project seeks to directly transfer money to a bank account in order to combat corruption. The discussions highlighted that this was nothing but introducing a neo liberal thrust in social policy and that it was not feasible for various reasons. First, 95% of rural India did not have functioning banks and banks are quite far away. Second, in order to combat this dearth of banks the idea of business correspondents, who handled banking transactions and helped in opening of bank accounts, had been introduced which had created various problems. The Reserve Bank of India reported that there was dearth of business correspondents as there was very little incentive to become one; their salary is merely INR 4000. Third, there were concerns about how an aadhaar number was considered a valid document for Know Your Customer (KYC) checks. There was a requirement for scrutiny and auditing of documents submitted during the time of enrolment which, in the present scheme of things, could not be verified. Fourth, there were no restrictions on number of bank accounts that could be opened with a single aadhaar number which gave rise to a possibility of opening multiple and shell accounts on a single aadhaar number. Therefore, records only showed transactions when money was transferred from an aadhaar number to another aadhaar number as opposed to an account-to-account transfer. The discussion relied on NPCI data which shows which bank an aadhaar number is associated with but does not show if a transaction by an aadhaar number is overwritten by another bank account belonging to the same aadhaar number.</p>
<h3 id="6" style="text-align: justify;"><strong>6. Surveillance and UIDAI</strong></h3>
<p style="text-align: justify;">The participants had discussed the possibility of an alternative purpose for enrolling Aadhaar in the session titled ‘Privacy, Surveillance, and Ethical Dimensions of Aadhaar’. The discussion traced the history of this project to gain insight on this issue. We have summarised below the key take aways from this discussion.</p>
<p style="text-align: justify;">There are claims that the main objective of launching the UID Project is not to facilitate implementation of social security schemes but to collect personal (financial and non-financial) information of the citizens and residents of the country to build a data monopoly. For this purpose, PDS was chosen as a suitable social security scheme as it has the largest coverage. Several participants suggested that numerous reports authored by FICCI, KPMG and ASSOCHAM contained proposals for establishing a national identity authority which threw some light on the commercial intentions behind information collection under the UID Project.</p>
<p style="text-align: justify;">It was also pointed out that there was documented proof that information collected under the UID Project might have been shared with foreign companies. There are suggestions about links established between proponents of the UID Project and companies backed by CIA or the French Government which run security projects and deal in data sharing in several jurisdictions.</p>
<h3 id="7" style="text-align: justify;"><strong>7. Strategies for Future Action</strong></h3>
<p>The participants laid down a list of measures that must be taken to take the discussions forward. We have enumerated these recommendations below.</p>
<ul>
<li>Prepare and compile an anthology of articles as an output of this workshop. </li>
<li>Prepare position papers on specific issues related to the UID Project </li>
<li>Prepare pamphlets/brochures on issues with the UID Project for public consumption </li>
<li>Prepare counter-advertisements for Aadhaar</li>
<li>Publish existing empirical evidence on the flaws in Aadhaar.</li>
<li>Set up an online portal dedicated to providing updates on the UID Project and allows discussions on specific issues related to Aadhaar.</li>
<li>Use Social Media to reach out to the public. Regularly track and comment on social media pages of relevant departments of the government.</li>
<li>Create groups dedicated to research and advocacy of specific aspects of the UID Project. </li>
<li>Create a Coordination Committee preferably based in Delhi which would be responsible for regularly holding meetings and for preparing a coordinated plan of action. Employ permanent to staff to run the Committee.</li>
<li>Organise an advocacy campaign against use of Aadhaar in collaboration with other organisations and build public domain acceptance. </li>
<li>The campaign must specifically focus on the unfettered scope of UID and expanse, misrepresentation of the success of Aadhaar by highlighting real savings, technological flaws, status of pilot programs and increasing corruption on account of the UID Project</li>
<li>Prepare a statement of public concern regarding the UID Project and collect signatures from eminent persons including academics, technical experts, civil society groups and members of parliament.</li>
<li>Organise events and discussions on issues relating to Aadhaar and invite members og government departments to speak and discuss the issues. </li>
<li style="text-align: justify;">Write to Members of Parliament and Members of Legislative Assemblies raising questions on their or their parties’ support for Aadhaar and silence on the problems created by the UID Project. </li>
<li style="text-align: justify;">Organise public hearings in states like Rajasthan to observe and document ground realities of the UID Project and share these outcomes with the state government and media. </li>
<li>Plan a national social audit and public hearing on the working of UID Project in the country. </li>
<li style="text-align: justify;">File Contempt Petitions in the Supreme Court and High Courts against mandatory use of Aadhaar number for services not allowed by the Supreme Court. </li>
<li style="text-align: justify;">Reach out to and engage with various foreign citizens and organisations that have been fighting on similar issues. The organisations and individuals who could be approached would include EPIC, Electronic Frontier foundation, David Moss, UK, Roger Clarke, Australia, Prof. Ian Angel, Snowden, Assange and Chomsky.</li>
<li style="text-align: justify;">Work towards increasing awareness about the UID Project and gaining support from the student and research community, student organisations, trade unions, and other associations and networks in the unorganised sector.</li></ul>
<h3 id="AA" style="text-align: justify;"><strong>Annexure A – Workshop Agenda</strong></h3>
<h4>May 26, 2016</h4>
<table>
<tbody>
<tr>
<td>
<p>9:00-9:30</p>
</td>
<td>
<p><strong>Registration</strong></p>
</td>
</tr>
<tr>
<td>
<p>9:30-10:00</p>
</td>
<td>
<p>Prof. Dinesh Abrol - <em>Welcome</em><br />
<em>Self-introduction and expectations of participants</em><br />
Dr. Usha Ramanathan - <em>Overview of the Workshop</em></p>
</td>
</tr>
<tr>
<td>
<p>10:00-11:00</p>
</td>
<td>
<p><strong>Session 1: Current Status of Aadhaar</strong><br />
Dr. Usha Ramanathan, Legal Researcher, New Delhi - <em>What the 2016 Law Says, and How it Came into Being</em><br />
S. Prasanna, Advocate, New Delhi - <em>Status and Force of Supreme Court Orders on Aadhaar</em><br /> <em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>11:00-11:30</p>
</td>
<td>
<p><strong>Tea Break</strong></p>
</td>
</tr>
<tr>
<td>
<p>11:30-13:30</p>
</td>
<td>
<p><strong>Session 2: Direct Benefits Transfers</strong><br />
Prof. Reetika Khera, Indian Institute of Technology, Delhi - <em>Welfare Needs Aadhaar like a Fish Needs a Bicycle</em><br />
Prof. R. Ramakumar, Tata Institute of Social Sciences, Mumbai - <em>Aadhaar and the Social Sector: A critical analysis of the claims of benefits and inclusion</em><br />
Ashok Rao, Delhi Science Forum - <em>Cash Transfers Study</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>13:30-14:30</p>
</td>
<td>
<p><strong>Lunch</strong></p>
</td>
</tr>
<tr>
<td>
<p>14:30-16:00</p>
</td>
<td>
<p><strong>Session 3: Aadhaar: Science, Technology, and Security</strong><br />
Prof. Subashis Banerjee, Dept of Computer Science & Engineering, IIT, Delhi - <em>Privacy and Security Issues Related to the Aadhaar Act</em><br />
Pukhraj Singh, Former National Cyber Security Manager, Aadhaar, New Delhi - <em>Aadhaar: Security and Surveillance Dimensions</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>16:00-16:30</p>
</td>
<td>
<p><strong>Tea Break</strong></p>
</td>
</tr>
<tr>
<td>
<p>16:30-17:30</p>
</td>
<td>
<p><strong>Session 4: Aadhaar - International Dimensions</strong><br />
Joshita Pai, Center for Communication Governance, National Law University, Delhi - <em>Biometrics and Mandatory IDs in Other Parts of the World</em><br />
Dr. Gopal Krishna, Citizens Forum for Civil Liberties - <em>International Dimensions of Aadhaar</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>17:30-18:00</p>
</td>
<td>
<p><strong>High Tea</strong></p>
</td>
</tr>
</tbody>
</table>
<h4>May 27, 2016</h4>
<table>
<tbody>
<tr>
<td>
<p>9:30-11:00</p>
</td>
<td>
<p><strong>Session 5: Privacy, Surveillance and Ethical Dimensions of Aadhaar</strong><br />
Prabir Purkayastha, Free Software Movement of India, New Delhi - <em>Surveillance Capitalism and the Commodification of Personal Data</em><br />
Arjun Jayakumar, SFLC - <em>Surveillance Projects Amalgamated</em><br />
Col Mathew Thomas, Bengaluru - <em>The Deceit of Aadhaar<em></em><br />
<em>Discussion</em></em></p>
<em>
</em></td>
</tr>
<tr>
<td>
<p>11:00-11:30</p>
</td>
<td>
<p><strong>Tea Break</strong></p>
</td>
</tr>
<tr>
<td>
<p><em>11:30-13:00</em></p>
</td>
<td>
<p><strong>Session 6: Aadhaar - Broad Issues I</strong><br />
Prof. G Nagarjuna, Homi Bhabha Center for Science Education, Tata Institute of Fundamental Research, Mumbai - <em>How to prevent linked data in the context of Aadhaar</em><br />
Dr. Anupam Saraph, Pune - <em>Aadhaar and Moneylaundering</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>13:00-14:00</p>
</td>
<td>
<p><strong>Lunch</strong></p>
</td>
</tr>
<tr>
<td>
<p>14:00-15:30</p>
</td>
<td>
<p><strong>Session 7: Aadhaar - Broad Issues II</strong><br />
Prof. MS Sriram, Visiting Faculty, Indian Institute of Management, Bangalore - <em>Financial lnclusion</em><br />
Nikhil Dey, MKSS, Rajasthan - <em>Field witness: Technology on the Ground</em><br />
Prof. Himanshu, Centre for Economic Studies & Planning, JNU - <em>UID Process and Financial Inclusion</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>15:30-16:00</p>
</td>
<td>
<p><strong>Session 8: Conclusion</strong></p>
</td>
</tr>
<tr>
<td>
<p>16:00-18:00</p>
</td>
<td>
<p><strong>Informal Meetings</strong></p>
</td>
</tr>
</tbody>
</table>
<h3 id="AB" style="text-align: justify;"><strong>Annexure B – Workshop Participants</strong></h3>
<p>Anjali Bhardwaj, Satark Nagrik Sangathan</p>
<p>Dr. Anupam Saraph</p>
<p>Arjun Jayakumar, Software Freedom Law Centre</p>
<p>Ashok Rao, Delhi Science Forum</p>
<p>Prof. Chinmayi Arun, National Law University, Delhi</p>
<p>Prof. Dinesh Abrol, Jawaharlal Nehru University</p>
<p>Prof. G Nagarjuna, Homi Bhabha Center for Science Education, Tata Institute of Fundamental Research, Mumbai</p>
<p>Dr. Gopal Krishna, Citizens Forum for Civil Liberties</p>
<p>Prof. Himanshu, Jawaharlal Nehru University</p>
<p>Japreet Grewal, the Centre for Internet and Society</p>
<p>Joshita Pai, National Law University, Delhi</p>
<p>Malini Chakravarty, Centre for Budget and Governance Accountability</p>
<p>Col. Mathew Thomas</p>
<p>Prof. MS Sriram, Indian Institute of Management, Bangalore</p>
<p>Nikhil Dey, Mazdoor Kisan Shakti Sangathan</p>
<p>Prabir Purkayastha, Knowledge Commons and Free Software Movement of India</p>
<p>Pukhraj Singh, Bhujang</p>
<p>Rajiv Mishra, Jawaharlal Nehru University</p>
<p>Prof. R Ramakumar, Tata Institute of Social Sciences, Mumbai</p>
<p>Dr. Reetika Khera, Indian Institute of Technology, Delhi</p>
<p>Dr. Ritajyoti Bandyopadhyay, Indian Institute of Science Education and Research, Mohali</p>
<p>S. Prasanna, Advocate</p>
<p>Sanjay Kumar, Science Journalist</p>
<p>Sharath, Software Freedom Law Centre</p>
<p>Shivangi Narayan, Jawaharlal Nehru University</p>
<p>Prof. Subhashis Banerjee, Indian Institute of Technology, Delhi</p>
<p>Sumandro Chattapadhyay, the Centre for Internet and Society</p>
<p>Dr. Usha Ramanathan, Legal Researcher</p>
<p><em>Note: This list is only indicative, and not exhaustive.</em></p>
<hr />
<p><a name="ftn1"><strong>[1]</strong></a> Civil Appeal No. 4853 of 2014</p>
<p><a name="ftn2"><strong>[2]</strong></a> WP(C) 494/2012</p>
<p><a name="ftn3"><strong>[3]</strong> </a>. WP(C) 829/2013</p>
<p><a name="ftn4"><strong>[4]</strong></a> WP(C) 833/2013</p>
<p><a name="ftn5"><strong>[5]</strong></a> WP (C) 37/2015; (Earlier intervened in the Aruna Roy petition in 2013)</p>
<p><a name="ftn6"><strong>[6]</strong></a> WP (C) 932/2015</p>
<p><a name="ftn7"><strong>[7]</strong></a> Transferred from Madras HC 2013.</p>
<p style="text-align: justify;"><a name="ftn8"><strong>[8]</strong></a> SLP (Crl) 2524/2014 filed against the order of the Goa Bench of the Bombay HC in CRLWP 10/2014 wherein the High Court had directed UIDAI to share biometric information held by them of all residents of a particular place in Goa to help with a criminal investigation in a case involving charges of rape and sexual assault.</p>
<p><a name="ftn9"><strong>[9]</strong></a> See :http://scroll.in/article/806243/rajasthan-presses-on-with-aadhaar-after-fingerprint-readers-fail-well-buy-iris-scanners</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges'>http://editors.cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges</a>
</p>
No publisherJapreet Grewal, Vanya Rakesh, Sumandro Chattapadhyay, and Elonnai HickockBig DataData SystemsPrivacyResearchers at WorkInternet GovernanceAadhaarWelfare GovernanceBiometricsBig Data for DevelopmentUID2019-03-16T04:42:52ZBlog EntryLeveraging Mobile Network Big Data for Development Policy: Opportunities & Challenges
http://editors.cis-india.org/internet-governance/news/leveraging-mobile-network-big-data-for-development-policy-opportunities-challenges
<b>Amber Sinha participated in this event held at IRDC, New Delhi on November 2, 2015. The event was organized by LIRNEasia.</b>
<p style="text-align: justify; ">As part of the International Development Research Centre (IDRC) distinguished lecture series, <a href="http://lirneasia.net/about/profiles/sriganesh-lokanathan/">Sriganesh Lokanathan</a>, Team Leader- Big Data Research at LIRNEasia gave a talk in Delhi (Ramalingaswami Conference Hall, International Development Research Centre, 208 Jor Bagh, New Delhi 110003) on Monday, 2nd November 2015. Sriganesh spoke on the topic of “Leveraging mobile network big data for developmental policy: opportunities & challenges.”</p>
<p dir="ltr"><b>Program</b>:</p>
<p dir="ltr"><span class="aBn"><span class="aQJ">11.00 a.m.</span></span>: Welcome and introductions: Dr. Anindya Chatterjee, Asia Regional Director, IDRC</p>
<p dir="ltr">11.05 a.m.: Talk by Mr Sriganesh Lokanathan, Team Leader, Big Data Research, LIRNEasia, Sri Lanka</p>
<p dir="ltr"><span class="aBn"><span class="aQJ">11.40 a.m.:</span></span> Discussions and Q & A</p>
<p dir="ltr"><span class="aBn"><span class="aQJ">12.15 p.m.:</span></span> Closing remarks: Phet Sayo, Senior Program Officer, IDRC</p>
<p dir="ltr">See the programme details published by <a class="external-link" href="http://lirneasia.net/2015/10/lirneasia-big-data-team-lead-to-talk-at-idrc-india/comment-page-1/">LIRNEasia</a>.</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/leveraging-mobile-network-big-data-for-development-policy-opportunities-challenges'>http://editors.cis-india.org/internet-governance/news/leveraging-mobile-network-big-data-for-development-policy-opportunities-challenges</a>
</p>
No publisherpraskrishnaInternet GovernanceBig Data2015-12-16T01:31:11ZNews ItemBenefits and Harms of "Big Data"
http://editors.cis-india.org/internet-governance/blog/benefits-and-harms-of-big-data
<b>Today the quantity of data being generated is expanding at an exponential rate. From smartphones and televisions, trains and airplanes, sensor-equipped buildings and even the infrastructures of our cities, data now streams constantly from almost every sector and function of daily life.</b>
<h3 style="text-align: justify; "><b>Introduction</b></h3>
<p style="text-align: justify; ">In 2011 it was estimated that the quantity of data produced globally would surpass 1.8 zettabyte<a href="#_ftn1" name="_ftnref1">[1]</a>. By 2013 that had grown to 4 zettabytes<a href="#_ftn2" name="_ftnref2">[2]</a>, and with the nascent development of the so-called 'Internet of Things' gathering pace, these trends are likely to continue. This expansion in the volume, velocity, and variety of data available<a href="#_ftn3" name="_ftnref3">[3]</a> , together with the development of innovative forms of statistical analytics, is generally referred to as "Big Data"; though there is no single agreed upon definition of the term. Although still in its initial stages, Big Data promises to provide new insights and solutions across a wide range of sectors, many of which would have been unimaginable even 10 years ago.</p>
<p style="text-align: justify; ">Despite enormous optimism about the scope and variety of Big Data's potential applications however, many remain concerned about its widespread adoption, with some scholars suggesting it could generate as many harms as benefits<a href="#_ftn4" name="_ftnref4">[4]</a>. Most notably these have included concerns about the inevitable threats to privacy associated with the generation, collection and use of large quantities of data <a href="#_ftn5" name="_ftnref5">[5]</a>. However, concerns have also been raised regarding, for example, the lack of transparency around the design of algorithms used to process the data, over-reliance on Big Data analytics as opposed to traditional forms of analysis and the creation of new digital divides to just name a few.</p>
<p style="text-align: justify; ">The existing literature on Big Data is vast, however many of the benefits and harms identified by researchers tend to relate to sector specific applications of Big Data analytics, such as predictive policing, or targeted marketing. Whilst these examples can be useful in demonstrating the diversity of Big Data's possible applications, it can nevertheless be difficult to gain an overall perspective of the broader impacts of Big Data as a whole. As such this article will seek to disaggregate the potential benefits and harms of Big Data, organising them into several broad categories, which are reflective of the existing scholarly literature.</p>
<h3 style="text-align: justify; "><b>What are the potential benefits of Big Data?</b></h3>
<p style="text-align: justify; ">From politicians to business leaders, recent years have seen Big Data confidently proclaimed as a potential solution to a diverse range of problems from, world hunger and diseases, to government budget deficits and corruption. But if we look beyond the hyperbole and headlines, what do we really know about the advantages of Big Data? Given the current buzz surrounding it, the existing literature on Big Data is perhaps unsurprisingly vast, providing innumerable examples of the potential applications of Big Data from agriculture to policing. However, rather than try (and fail) to list the many possible applications of Big Data analytics across all sectors and industries, for the purposes of this article we have instead attempted to distil the various advantages of Big Data discussed within literature into the following five broad categories; Decision-Making, Efficiency & Productivity, Research & Development, Personalisation and Transparency, each of which will be discussed separately below.</p>
<p style="text-align: justify; "><i>Decision-Making </i></p>
<p style="text-align: justify; ">Whilst data analytics have always been used to improve the quality and efficiency of decision-making processes, the advent of Big Data means that the areas of our lives in which data driven decision- making plays a role is expanding dramatically; as businesses and governments become better able to exploit new data flows. Furthermore, the real-time and predictive nature of decision-making made possible by Big Data, are increasingly allowing these decisions to be automated. As a result, Big Data is providing governments and business with unprecedented opportunities to create new insights and solutions; becoming more responsive to new opportunities and better able to act quickly - and in some cases preemptively - to deal with emerging threats.</p>
<p style="text-align: justify; ">This ability of Big Data to speed up and improve decision-making processes can be applied across all sectors from transport to healthcare and is often cited within the literature as one of the key advantages of Big Data. Joh, for example, highlights the increased use of data driven predictive analysis by police forces to help them to forecast the times and geographical locations in which crimes are most likely to occur. This allows the force to redistribute their officers and resources according to anticipated need, and in certain cities has been highly effective in reducing crime rates <a href="#_ftn6" name="_ftnref6">[6]</a>. Raghupathi meanwhile cites the case of healthcare, where predictive modelling driven by big data is being used to proactively identify patients who could benefit from preventative care or lifestyle changes<a href="#_ftn7" name="_ftnref7">[7]</a>.</p>
<p style="text-align: justify; ">One area in particular where the decision-making capabilities of Big Data are having a significant impact is in the field of risk management <a href="#_ftn8" name="_ftnref8">[8]</a>. For instance, Big Data can allow companies to map their entire data landscape to help detect sensitive information, such as 16 digit numbers - potentially credit card data - which are not being stored according to regulatory requirements and intervene accordingly. Similarly, detailed analysis of data held about suppliers and customers can help companies to identify those in financial trouble, allowing them to act quickly to minimize their exposure to any potential default<a href="#_ftn9" name="_ftnref9">[9]</a>.</p>
<p style="text-align: justify; "><i>Efficiency and Productivity </i></p>
<p style="text-align: justify; ">In an era when many governments and businesses are facing enormous pressures on their budgets, the desire to reduce waste and inefficiency has never been greater. By providing the information and analysis needed for organisations to better manage and coordinate their operations, Big Data can help to alleviate such problems, leading to the better utilization of scarce resources and a more productive workforce <a href="#_ftn10" name="_ftnref10">[10]</a>.</p>
<p style="text-align: justify; ">Within the literature such efficiency savings are most commonly discussed in relation to reductions in energy consumption <a href="#_ftn11" name="_ftnref11">[11]</a>. For example, a report published by Cisco notes how the city of Olso has managed to reduce the energy consumption of street-lighting by 62 percent through the use of smart solutions driven by Big Data<a href="#_ftn12" name="_ftnref12">[12]</a>. Increasingly, however, statistical models generated by Big Data analytics are also being utilized to identify potential efficiencies in sourcing, scheduling and routing in a wide range of sectors from agriculture to transport. For example, Newell observes how many local governments are generating large databases of scanned license plates through the use of automated license plate recognition systems (ALPR), which government agencies can then use to help improve local traffic management and ease congestion<a href="#_ftn13" name="_ftnref13">[13]</a>.</p>
<p style="text-align: justify; ">Commonly these efficiency savings are only made possible by the often counter-intuitive insights generated by the Big Data models. For example, whilst a human analyst planning a truck route would always tend to avoid 'drive-bys' - bypassing one stop to reach a third before doubling back - Big Data insights can sometimes show such routes to be more efficient. In such cases efficiency saving of this kind would in all likelihood have gone unrecognised by a human analyst, not trained to look for such patterns<a href="#_ftn14" name="_ftnref14">[14]</a>.</p>
<p style="text-align: justify; "><i>Research, Development, and Innovation</i></p>
<p style="text-align: justify; ">Perhaps one of the most intriguing benefits of Big Data is its potential use in the research and development of new products and services. As is highlighted throughout the literature, Big Data can help businesses to gain an understanding of how others perceive their products or identify customer demand and adapt their marketing or indeed the design of their products accordingly<a href="#_ftn15" name="_ftnref15">[15]</a>. Analysis of social media data, for instance, can provide valuable insights into customers' sentiments towards existing products as well as discover demands for new products and services, allowing businesses to respond more quickly to changes in customer behaviour<a href="#_ftn16" name="_ftnref16">[16]</a>.</p>
<p style="text-align: justify; ">In addition to market research, Big Data can also be used during the design and development stage of new products; for example by helping to test thousands of different variations of computer-aided designs in an expedient and cost-effective manner. In doing so, business and designers are able to better assess how minor changes to a products design may affect its cost and performance, thereby improving the cost-effectiveness of the production process and increasing profitability.</p>
<p style="text-align: justify; "><i>Personalisation</i></p>
<p style="text-align: justify; ">For many consumers, perhaps the most familiar application of Big Data is its ability to help tailor products and services to meet their individual preferences. This phenomena is most immediately noticeable on many online services such as Netflix; where data about users activities and preferences is collated and analysed to provide a personalised service, for example by suggesting films or television shows the user may enjoy based upon their previous viewing history<a href="#_ftn17" name="_ftnref17">[17]</a>. By enabling companies to generate in-depth profiles of their customers, Big Data allows businesses to move past the 'one size fits all' approach to product and services design and instead quickly and cost-effectively adapt their services to better meet customer demand.</p>
<p style="text-align: justify; ">In addition to service personalisation, similar profiling techniques are increasingly being utilized in sectors such as healthcare. Here data about a patient's medical history, lifestyle, and even their gene expression patterns are collated, generating a detailed medical profile which can then be used to tailor treatments to meet their specific needs<a href="#_ftn18" name="_ftnref18">[18]</a>. Targeted care of this sort can not only help to reduce costs for example by helping to avoid over-prescriptions, but may also help to improve the effectiveness of treatments and so ultimately their outcome.</p>
<p style="text-align: justify; "><i>Transparency </i></p>
<p style="text-align: justify; ">If 'knowledge is power', then, - so say Big Data enthusiasts - advances in data analytics and the quantity of data available can give consumers and citizens the knowledge to hold governments and businesses to account, as well as make more informed choices about the products and services they use. Nevertheless, data (even lots of it) does not necessarily equal knowledge. In order for citizens and consumers to be able to fully utilize the vast quantities of data available to them, they must first have some way to make sense of it. For some, Big Data analytics provides just such a solution, allowing users to easily search, compare and analyze available data, thereby helping to challenge existing information asymmetries and make business and government more transparent<a href="#_ftn19" name="_ftnref19">[19]</a>.</p>
<p style="text-align: justify; ">In the private sector, Big Data enthusiasts have claimed that Big Data holds the potential to ensure complete transparency of supply chains, enabling concerned consumers to trace the source of their products, for example to ensure that they have been sourced ethically <a href="#_ftn20" name="_ftnref20">[20]</a>. Furthermore, Big Data is now making accessible information which was previously unavailable to average consumers and challenging companies whose business models rely on the maintenance of information asymmetries.The real-estate industry, for example, relies heavily upon its ability to acquire and control proprietary information, such as transaction data as a competitive asset. In recent years, however, many online services have allowed consumers to effectively bypass agents, by providing alternative sources of real-estate data and enabling prospective buyers and sellers to communicate directly with each other<a href="#_ftn21" name="_ftnref21">[21]</a>. Therefore, providing consumers with access to large quantities of actionable data . Big Data can help to eliminate established information asymmetries, allowing them to make better and more informed decisions about the products they buy and the services they enlist.</p>
<p style="text-align: justify; ">This potential to harness the power of Big Data to improve transparency and accountability can also be seen in the public sector, with many scholars suggesting that greater access to government data could help to stem corruption and make politics more accountable. This view was recently endorsed by the UN who highlighted the potential uses of Big Data to improve policymaking and accountability in a report published by the Independent Expert Advisory Group on the "Data Revolution for Sustainable Development". In the report experts emphasize the potential of what they term the 'data revolution', to help achieve sustainable development goals by for example helping civil society groups and individuals to 'develop data literacy and help communities and individuals to generate and use data, to ensure accountability and make better decisions for themselves' <a href="#_ftn22" name="_ftnref22">[22]</a>.</p>
<h3 style="text-align: justify; "><b>What are the potential harms of Big Data?</b></h3>
<p style="text-align: justify; ">Whilst it is often easy to be seduced by the utopian visions of Big Data evangelists, in order to ensure that Big Data can deliver the types of far-reaching benefits its proponents promise, it is vital that we are also sensitive to its potential harms. Within the existing literature, discussions about the potential harms of Big Data are perhaps understandably dominated by concerns about privacy. Yet as Big Data has begun to play an increasingly central role in our daily lives, a broad range of new threats have begun to emerge including issues related to security and scientific epistemology, as well as problems of marginalisation, discrimination and transparency; each of which will be discussed separately below.</p>
<h2 style="text-align: justify; ">Privacy</h2>
<p style="text-align: justify; ">By far the biggest concern raised by researchers in relation to Big Data is its risk to privacy. Given that by its very nature Big Data requires extensive and unprecedented access to large quantities of data; it is hardly surprising that many of the benefits outlined above in one way or another exist in tension with considerations of privacy. Although many scholars have called for a broader debate on the effects of Big Data on ethical best practice <a href="#_ftn23" name="_ftnref23"><sup><sup>[23]</sup></sup></a>, a comprehensive exploration into the complex debates surrounding the ethical implications of Big Data go far beyond the scope of this article. Instead we will simply attempt to highlight some of the major areas of concern expressed in the literature, including its effects on established principles of privacy and the implication of Big Data on the suitability of existing regulatory frameworks governing privacy and data protection.</p>
<p style="text-align: justify; ">1. Re-identification</p>
<p style="text-align: justify; ">Traditionally many Big Data enthusiasts have used de-identification - the process of anonymising data by removing personally identifiable information (PII) - as a way of justifying mass collection and use of personal data. By claiming that such measures are sufficient to ensure the privacy of users, data brokers, companies and governments have sought to deflect concerns about the privacy implications of Big Data, and suggest that it can be compliant with existing regulatory and legal frameworks on data protection.</p>
<p style="text-align: justify; ">However, many scholars remain concerned about the limits of anonymisation. As Tene and Polonetsky observe 'Once data-such as a clickstream or a cookie number-are linked to an identified individual, they become difficult to disentangle'<a href="#_ftn24" name="_ftnref24">[24]</a>. They cite the example of University of Texas researchers Narayanan and Shmatikov, who were able to successfully re-identify anonymised Netflix user data by cross referencing it with data stored in a publicly accessible online database. As Narayanan and Shmatikov themselves explained, 'once any piece of data has been linked to a person's real identity, any association between this data and a virtual identity breaks anonymity of the latter' <a href="#_ftn25" name="_ftnref25">[25]</a>. The quantity and variety of datasets which Big Data analytics has made associable with individuals is therefore expanding the scope of the types of data that can be considered PII, as well as undermining claims that de-identification alone is sufficient to ensure privacy for users.</p>
<p style="text-align: justify; ">2. Privacy Frameworks Obsolete?</p>
<p style="text-align: justify; ">In recent decades privacy and data protection frameworks based upon a number of so-called 'privacy principles' have formed the basis of most attempts to encourage greater consideration of privacy issues online<a href="#_ftn26" name="_ftnref26">[26]</a>. For many however, the emergence of Big Data has raised question about the extent to which these 'principles of privacy' are workable in an era of ubiquitous data collection.</p>
<p style="text-align: justify; "><i>Collection Limitation and Data Minimization</i> : Big Data by its very nature requires the collection and processing of very large and very diverse data sets. Unlike other forms scientific research and analysis which utilize various sampling techniques to identify and target the types of data most useful to the research questions, Big Data instead seeks to gather as much data as possible, in order to achieve full resolution of the phenomenon being studied, a task made much easier in recent years as a result of the proliferation of internet enabled devices and the growth of the Internet of Things. This goal of attaining comprehensive coverage exists in tension however with the key privacy principles of collection limitation and data minimization which seek to limit both the quantity and variety of data collected about an individual to the absolute minimum<a href="#_ftn27" name="_ftnref27">[27]</a>.</p>
<p style="text-align: justify; "><i>Purpose Limitation:</i> Since the utility of a given dataset is often not easily identifiable at the time of collection, datasets are increasingly being processed several times for a variety of different purposes. Such practices have significant implications for the principle of purpose limitation, which aims to ensure that organizations are open about their reasons for collecting data, and that they use and process the data for no other purpose than those initially specified <a href="#_ftn28" name="_ftnref28">[28]</a>.</p>
<p style="text-align: justify; "><i>Notice and Consent: </i> The principles of notice and consent have formed the cornerstones of attempts to protect privacy for decades. Nevertheless in an era of ubiquitous data collection, the notion that an individual must be required to provide their explicit consent to allow for the collection and processing of their data seems increasingly antiquated, a relic of an age when it was possible to keep track of your personal data relationships and transactions. Today as data streams become more complex, some have begun to question suitability of consent as a mechanism to protect privacy. In particular commentators have noted how given the complexity of data flows in the digital ecosystem most individuals are not well placed to make truly informed decisions about the management of their data<a href="#_ftn29" name="_ftnref29">[29]</a>. In one study, researchers demonstrated how by creating the perceptions of control, users were more likely to share their personal information, regardless of whether or not the users had actually gained control <a href="#_ftn30" name="_ftnref30">[30]</a>. As such, for many, the garnering of consent is increasingly becoming a symbolic box-ticking exercise which achieves little more than to irritate and inconvenience customers whilst providing a burden for companies and a hindrance to growth and innovation <a href="#_ftn31" name="_ftnref31">[31]</a>.</p>
<p style="text-align: justify; "><i>Access and Correction:</i> The principle of 'access and correction' refers to the rights of individuals to obtain personal information being held about them as well as the right to erase, rectify, complete or otherwise amend that data. Aside from the well documented problems with privacy self-management, for many the real-time nature of data generation and analysis in an era of Big Data poses a number of structural challenges to this principle of privacy. As x comments, 'a good amount of data is not pre-processed in a similar fashion as traditional data warehouses. This creates a number of potential compliance problems such as difficulty erasing, retrieving or correcting data. A typical big data system is not built for interactivity, but for batch processing. This also makes the application of changes on a (presumably) static data set difficult'<a href="#_ftn32" name="_ftnref32">[32]</a>.</p>
<p style="text-align: justify; "><i>Opt In-Out:</i> The notion that the provision of data should be a matter of personal choice on the part of the individual and that the individual can, if they chose decide to 'opt-out' of data collection, for example by ceasing use of a particular service, is an important component of privacy and data protection frameworks. The proliferation of internet-enabled devices, their integration into the built environment and the real-time nature of data collection and analysis however are beginning to undermine this concept. For many critics of Big Data the ubiquity of data collection points as well as the compulsory provision of data as a prerequisite for the access and use of many key online services is making opting-out of data collection not only impractical but in some cases impossible. <a href="#_ftn33" name="_ftnref33">[33]</a></p>
<p style="text-align: justify; ">3. "Chilling Effects"</p>
<p style="text-align: justify; ">For many scholars the normalization of large scale data collection is steadily producing a widespread perception of ubiquitous surveillance amongst users. Drawing upon Foucault's analysis of Jeremy Bentham's panopticon and the disciplinary effects of surveillance, they argue that this perception of permanent visibility can cause users to sub-consciously 'discipline' and self- regulate of their own behavior, fearful of being targeted or identified as 'abnormal' <a href="#_ftn34" name="_ftnref34">[34]</a>. As a result, the pervasive nature of Big Data risks generating a 'chilling effect' on user behavior and free speech.</p>
<p style="text-align: justify; ">Although the notion of "chilling effects" is quite prevalent throughout the academic literature on surveillance and security, the difficulty of quantifying the perception and effects of surveillance on online behavior and practices means that there have only been a limited number of empirical studies of this phenomena, and none directly related to the chilling effects of Big Data. One study, conducted by researchers at MIT however, sought to assess the impact of Edward Snowden's revelations about NSA surveillance programs on Google search trends. Nearly 6,000 participants were asked to individually rate certain keywords for their perceived degree of privacy sensitivity along multiple dimensions. Using Google's own publicly available search data, the researchers then analyzed search patterns for these terms before and after the Snowden revelations. In doing so they were able to demonstrate a reduction of around 2.2% in searchers for those terms deemed to be most sensitive in nature. According to the researchers themselves, the results 'suggest that there is a chilling effect on search behaviour from government surveillance on the Internet'<a href="#_ftn35" name="_ftnref35">[35]</a>. Although this study focussed on the effects on government surveillance, for many privacy advocates the growing pervasiveness of Big Data risks generating similar results. <a href="#_ftn36" name="_ftnref36">[36]</a></p>
<p style="text-align: justify; ">4. Dignitary Harms of Predictive Decision-Making</p>
<p style="text-align: justify; ">In addition to its potentially chilling effects on free speech, the automated nature of Big Data analytics also possess the potential to inflict so-called 'dignitary harms' on individuals, by revealing insights about themselves that they would have preferred to keep private <a href="#_ftn37" name="_ftnref37">[37]</a>.</p>
<p style="text-align: justify; ">In an infamous example, following a shopping trip to the retail chain Target, a young girl began to receive mail at her father's house advertising products for babies including, diapers, clothing, and cribs. In response, her father complained to the management of the company, incensed by what he perceived to be the company's attempts to "encourage" pregnancy in teens. A few days later however, the father was forced to contact the store again to apologies, after his daughter had confessed to him that she was indeed pregnant. It was later revealed that Target regularly analyzed the sale of key products such as supplements or unscented lotions in order to generate "pregnancy prediction" scores, which could be used to assess the likelihood that a customer was pregnant and to therefore target them with relevant offers<a href="#_ftn38" name="_ftnref38">[38]</a>. Such cases, though anecdotal illustrate how Big Data if not adopted sensitively can lead to potential embarrassing information about users being made public.</p>
<h2 style="text-align: justify; ">Security</h2>
<p style="text-align: justify; ">In relation to cybersecurity Big Data can be viewed to a certain extent as a double-edged sword. On the one hand, the unique capabilities of Big Data analytics can provide organizations with new and innovative methods of enhancing their cybersecurity systems. On the other however, the sheer quantity and diversity of data emanating from a variety of sources creates its own security risks.</p>
<p style="text-align: justify; ">5. "Honey-Pot"</p>
<p style="text-align: justify; ">The larger the quantities of confidential information stored by companies on their databases the more attractive those databases may appear to potential hackers.</p>
<p style="text-align: justify; ">6. Data Redundancy and Dispersion</p>
<p style="text-align: justify; ">Inherent to Big Data systems is the duplication of data to many locations in order to optimize query processing. Data is dispersed across a wide range of data repositories in different servers, in different parts of the world. As a result it may be difficult for organizations to accurately locate and secure all items of personal information.</p>
<h3 style="text-align: justify; "><b>Epistemological and Methodological Implications</b></h3>
<p style="text-align: justify; ">In 2008 Chris Anderson infamously proclaimed the 'end of theory'. Writing for Wired Magazine, Anderson predicted that the coming age of Big Data would create a 'deluge of data' so large that the scientific methods of hypothesis, sampling and testing would be rendered 'obsolete' <a href="#_ftn39" name="_ftnref39">[39]</a>. 'There is now a better way' Anderson insisted, 'Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot'<a href="#_ftn40" name="_ftnref40">[40]</a>.</p>
<p style="text-align: justify; ">In spite of these bold claims however, many theorists remain skeptical of Big Data's methodological benefits and have expressed concern about its potential implications for conventional scientific epistemologies. For them the increased prominence of Big Data analytics in science does not signal a paradigmatic transition to a more enlightened data-driven age, but a hollowing out of the scientific method and an abandonment of casual knowledge in favor of shallow correlative analysis<a href="#_ftn41" name="_ftnref41">[41]</a>.</p>
<p style="text-align: justify; "><i>7. </i> Obfuscation <i></i></p>
<p style="text-align: justify; ">Although Big Data analytics can be utilized to study almost any phenomena where enough data exists, many theorists have warned that simply because Big Data analytics <i>can</i> be used does not necessarily mean that they <i>should</i> be used<a href="#_ftn42" name="_ftnref42">[42]</a>. Bigger is not always better and indeed the sheer quantity of data made available to users may in fact act to obscure certain insights. Whereas traditional scientific methods use sampling techniques to identify the most important and relevant data, Big Data by contrast encourages the collection and use of as much data as possible, in an attempt to attain full resolution of the phenomena being studied. However, not all data is equally useful and simply inputting as much data as possible into an algorithm is unlikely to produce accurate results and may instead obscure key insights.</p>
<p style="text-align: justify; ">Indeed, whilst the promise of automation is central to a large part of Big Data's appeal, researchers observe that most Big Data analysis still requires an element of human judgement to filter out the 'good' data from the 'bad', and to decide what aspects of the data are relevant to the research objectives. As Boyd and Crawford observe, 'in the case of social media data, there is a 'data cleaning' process: making decisions about what attributes and variables will be counted, and which will be ignored. This process is inherently subjective"<a href="#_ftn43" name="_ftnref43"><sup><sup>[43]</sup></sup></a>.</p>
<p style="text-align: justify; ">Google's Flu Trend project provides an illustrative example of how Big Data's tendency to try to maximise data inputs can produce misleading results. Designed to accurately track flu outbreaks based upon data collected from Google searches, the project was initially proclaimed to be a great success. Gradually however it became apparent that the results being produced were not reflective of the reality on the ground. Later it was discovered that the algorithms used by the project to interpret search terms were insufficiently accurate to filter out anomalies in searches, such as those related to the 2009 H1N1 flu pandemic. As such, despite the great promise of Big Data, scholars insist it remains critical to be mindful of its limitations, remain selective about the types of data included in the analysis and exercise caution and intuition whenever interpreting its results <a href="#_ftn44" name="_ftnref44"><sup><sup>[44]</sup></sup></a>.</p>
<p style="text-align: justify; ">8. "Apophenia"</p>
<p style="text-align: justify; ">In complete contrast to the problem of obfuscation, Boyd and Crawford observe how Big Data may also lead to the practice of 'apophenia', a phenomena whereby analysts interpret patterns where none exist, 'simply because enormous quantities of data can offer connections that radiate in all directions" <a href="#_ftn45" name="_ftnref45"><sup><sup>[45]</sup></sup></a>. David Leinweber for example demonstrated that data mining techniques could show strong but ultimately spurious correlations between changes in the S&P 500 stock index and butter production in Bangladesh <a href="#_ftn46" name="_ftnref46">[46]</a>. Such spurious correlation between disparate and unconnected phenomena are a common feature of Big Data analytics and risks leading to unfounded conclusions being draw from the data.</p>
<p style="text-align: justify; ">Although Leinweber's primary focus of analysis was the use of Data-Mining technologies, his observations are equally applicable to Big Data. Indeed the tendency amongst Big Data analysts to marginalise the types of domain specific expertise capable of differentiating between relevant and irrelevant correlations in favour of algorithmic automation can in many ways be seen to exacerbate many of the problems Leinweber identified.</p>
<p style="text-align: justify; "><i>9. </i> From Causation to Correlation</p>
<p style="text-align: justify; ">Closely related to the problem of Aphonenia is the concern that Big Data's emphasis on correlative analysis risks leading to an abandonment of the pursuit of causal knowledge in favour of shallow descriptive accounts of scientific phenomena<a href="#_ftn47" name="_ftnref47">[47]</a>.</p>
<p style="text-align: justify; ">For many, Big Data enthusiasts 'correlation is enough', producing inherently meaningful results interpretable by anyone without the need for pre-existing theory or hypothesis. Whilst proponents of Big Data claim that such an approach allows them to produce objective knowledge, by cleansing the data of any kind of philosophical or ideological commitment, for others by neglecting the knowledge of domain experts, Big Data risks generating a shallow type of analysis, since it fails to adequately embed observations within a pre-existing body of knowledge.</p>
<p style="text-align: justify; ">This commitment to an empiricist epistemology and methodological monism is particularly problematic in the context of studies of human behaviour, where actions cannot be calculated and anticipated using quantifiable data alone. In such instances, a certain degree of qualitative analysis of social, historical and cultural variables may be required in order to make the data meaningful by embedding it within a broader body of knowledge. The abstract and intangible nature of these variables requires a great deal of expert knowledge and interpretive skill to comprehend. It is therefore vital that the knowledge of domain specific experts is properly utilized to help 'evaluate the inputs, guide the process, and evaluate the end products within the context of value and validity'<a href="#_ftn48" name="_ftnref48">[48]</a>.</p>
<p style="text-align: justify; ">As such, although Big Data can provide unrivalled accounts of "what" people do, it fundamentally fails to deliver robust explanations of "why" people do it. This problem is especially critical in the case of public policy-making since without any indication of the motivations of individuals, policy-makers can have no basis upon which to intervene to incentivise more positive outcomes.</p>
<h3 style="text-align: justify; "><b>Digital Divides and Marginalisation</b></h3>
<p style="text-align: justify; ">Today data is a highly valuable commodity. The market for data in and of itself has been steadily growing in recent years with the business models of many online services now formulated around the strategy of harvesting data from users<a href="#_ftn49" name="_ftnref49"><sup><sup>[49]</sup></sup></a>. As with the commodification of anything however, inequalities can easily emerge between the haves and have not's. Whilst the quantity of data currently generated on a daily basis is many times greater than at any other point in human history, the vast majority of this data is owned and tightly controlled by a very small number of technology companies and data brokers. Although in some instances limited access to data may be granted to university researchers or to those willing and able to pay a fee, in many cases data remains jealously guarded by data brokers, who view it as an important competitive asset. As a result these data brokers and companies risk becoming the gatekeepers of the Big Data revolution, adjudicating not only over who can benefit from Big Data, but also in what context and under what terms. For many such inconsistencies and inequalities in access to data raises serious doubts about just how widely distributed the benefits of Big Data will be. Others go even further claiming that far from helping to alleviate inequalities, the advent of Big Data risks exacerbating already significant digital divides that exist as well as creating new ones <a href="#_ftn50" name="_ftnref50"><sup><sup>[50]</sup></sup></a>.</p>
<p style="text-align: justify; ">10. Anti-Competitive Practices</p>
<p style="text-align: justify; ">As a result of the reluctance of large companies to share their data, there increasingly exists a divide in access between small start-ups companies and their larger and more established competitors. Thus, new entrants to the marketplace may be at a competitive disadvantage in relation to large and well established enterprises, being as they are unable to harness the analytical power of the vast quantities of data available to large companies by virtue of their privileged market position. Since the performance of many online services are today often intimately connected with the collation and use of users data, some researchers have suggested that this inequity in access to data could lead to a reduction in competition in the online marketplace, and ultimately therefore to less innovation and choice for consumers<a href="#_ftn51" name="_ftnref51">[51]</a>.</p>
<p style="text-align: justify; ">As a result researchers including Nathan Newman of New York University have called for a reassessment and reorientation of anti-trust investigations and regulatory approaches more generally to 'to focus on how control of personal data by corporations can entrench monopoly power and harm consumer welfare in an economy shaped increasingly by the power of "big data"'<a href="#_ftn52" name="_ftnref52">[52]</a>. Similarly a report produced by the European Data Protection Supervisor concluded that, 'The scope for abuse of market dominance and harm to the consumer through refusal of access to personal information and opaque or misleading privacy policies may justify a new concept of consumer harm for competition enforcement in digital economy' <a href="#_ftn53" name="_ftnref53">[53]</a>.</p>
<p style="text-align: justify; ">11. Research</p>
<p style="text-align: justify; ">From a research perspective barriers to access to data caused by proprietary control of datasets are problematic, since certain types of research could become restricted to those privileged enough to be granted access to data. Meanwhile those denied access are left not only incapable of conducting similar research projects, but also unable to test, verify or reproduce the findings of those who do. The existence of such gatekeepers may also lead to reluctance on the part of researchers to undertake research critical of the companies, upon whom they rely for access, leading to a chilling effect on the types of research conducted<a href="#_ftn54" name="_ftnref54">[54]</a>.</p>
<p style="text-align: justify; ">12. Inequality</p>
<p style="text-align: justify; ">Whilst bold claims are regularly made about the potential of Big Data to deliver economic development and generate new innovations, some critics of remain concerned about how equally the benefits of Big Data will be distributed and the effects this could have on already established digital divides <a href="#_ftn55" name="_ftnref55">[55]</a>.</p>
<p style="text-align: justify; ">Firstly, whilst the power of Big Data is already being utilized effectively by most economically developed nations, the same cannot necessarily be said for many developing countries. A combination of lower levels of connectivity, poor information infrastructure, underinvestment in information technologies and a lack of skills and trained personnel make it far more difficult for the developing world to fully reap the rewards of Big Data. As a consequence the Big Data revolution risks deepening global economic inequality as developing countries find themselves unable to compete with data rich nations whose governments can more easily exploit the vast quantities of information generated by their technically literate and connected citizens.</p>
<p style="text-align: justify; ">Likewise, to the extent that the Big Data analytics is playing a greater role in public policy-making, the capacity of individuals to generate large quantities of data, could potentially impact upon the extent to which they can provide inputs into the policy-making process. In a country such as India for example, where there exist high levels of inequality in access to information and communication technologies and the internet, there remain large discrepancies in the quantities of data produced by individuals. As a result there is a risk that those who lack access to the means of producing data will be disenfranchised, as policy-making processes become configured to accommodate the needs and interests of a privilege minority <a href="#_ftn56" name="_ftnref56">[56]</a>.</p>
<h3 style="text-align: justify; "><b>Discrimination</b></h3>
<p style="text-align: justify; ">13. Injudicious or Discriminatory Outcomes</p>
<p style="text-align: justify; ">Big Data presents the opportunity for governments, businesses and individuals to make better, more informed decisions at a much faster pace. Whilst this can evidently provide innumerable opportunities to increase efficiency and mitigate risk, by removing human intervention and oversight from the decision-making process Big Data analysts run the risk of becoming blind to unfair or injudicious results generated by skewed or discriminatory programming of the algorithms.</p>
<p style="text-align: justify; ">There currently exists a large number of automated decision-making algorithms in operation across a broad range of sectors including most notably perhaps those used to asses an individual's suitability for insurance or credit. In either of these cases faults in the programming or discriminatory assessment criteria can have potentially damaging implications for the individual, who may as a result be unable to attain credit or insurance. This concern with the potentially discriminatory aspects of Big Data is prevalent throughout the literature and real life examples have been identified by researchers in a large number of major sectors in which Big Data is currently being used<a href="#_ftn57" name="_ftnref57">[57]</a>.</p>
<p style="text-align: justify; ">Yu for instance, cites the case of the insurance company Progressive, which required its customers to install 'Snapsnot' - a small monitoring device - into their cars in order to receive their best rates. The device tracked and reported the customers driving habits, and offered discounts to those drivers who drove infrequently, broke smoothly, and avoided driving at night - behaviors that correlate with a lower risk of future accidents. Although this form of price differentiation provided incentives for customers to drive more carefully, it also had the unintended consequence of unfairly penalizing late-night shift workers. As Yu observes, 'for late night shift-workers, who are disproportionately poorer and from minority groups, this differential pricing provides no benefit at all. It categorizes them as similar to late-night party-goers, forcing them to carry more of the cost of the intoxicated and other irresponsible driving that happens disproportionately at night'<a href="#_ftn58" name="_ftnref58">[58]</a>.</p>
<p style="text-align: justify; ">In another example, it is noted how Big Data is increasingly being used to evaluate applicants for entry-level service jobs. One method of evaluating applicants is by the length of their commute - the rationale being that employees with shorter commutes are statistically more likely to remain in the job longer. However, since most service jobs are typically located in town centers and since poorer neighborhoods tend to be those on the outskirts of town, such criteria can have the effect of unfairly disadvantaging those living in economically deprived areas. Consequently such metrics of evaluation can therefore also unintentionally act to reinforce existing social inequalities by making it more difficult for economically disadvantaged communities to work their way out of poverty<a href="#_ftn59" name="_ftnref59">[59]</a>.</p>
<p style="text-align: justify; ">14. Lack of Algorithmic Transparency.</p>
<p style="text-align: justify; ">If data is indeed the 'oil of the 21<sup>st</sup> century'<a href="#_ftn60" name="_ftnref60">[60]</a> then algorithms are very much the engines which are driving innovation and economic development. For many companies the quality of their algorithms is often a crucial factor in providing them with a market advantage over their competitor. Given their importance, the secrets behind the programming of algorithms are often closely guarded by companies, and are typically classified as trade secrets and as such are protected by intellectual property rights. Whilst companies may claim that such secrecy is necessary to encourage market competition and innovation, many scholars are becoming increasingly concerned about the lack of transparency surrounding the design of these most crucial tools.</p>
<p style="text-align: justify; ">In particular there is a growing sentiment common amongst many researchers that there currently exists a chronic lack of accountability and transparency in terms of how Big Data algorithms are programmed and what criteria are used to determine outcomes <a href="#_ftn61" name="_ftnref61"><sup><sup>[61]</sup></sup></a>. As Frank Pasquale observed,</p>
<p style="text-align: justify; "><i>'</i> <i> hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy'<a href="#_ftn62" name="_ftnref62"><b>[62]</b></a>. </i></p>
<p style="text-align: justify; ">As such, without increased transparency in algorithmic design, instances of Big Data discrimination may go unnoticed as analyst are unable to access the information necessary to identify them.</p>
<h3 style="text-align: justify; "><b>Conclusion</b></h3>
<p style="text-align: justify; ">Today Big Data presents us with as many challenges as it does benefits. Whilst Big Data analytics can offer incredible opportunities to reduce inefficiency, improve decision-making, and increase transparency, concerns remain about the effects of these new technologies on issues such as privacy, equality and discrimination. Although the tensions between the competing demands of Big Data advocates and their critics may appear irreconcilable; only by highlighting these points of contestation can we hope to begin to ask the types of important and difficult questions necessary to do so, including; how can we reconcile Big Data's need for massive inputs of personal information with core principles of privacy such as data minimization and collection limitation? What processes and procedures need to be put in place during the design and implementation of Big Data models and algorithms to provide sufficient transparency and accountability so as to avoid instances of discrimination? What measures can be used to help close digital divides and ensure that the benefits of Big Data are shared equitably? Questions such as these are today only just beginning to be addressed; each however, will require careful consideration and reasoned debate, if Big Data is to deliver on its promises and truly fulfil its 'revolutionary' potential.</p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1">[1]</a> Gantz, J., &Reinsel, D. Extracting Value from Chaos, <i>IDC, </i>(2011), available at: <a href="http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf"> http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf </a></p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2">[2]</a> Meeker, M. & Yu, L. Internet Trends, <i>Kleiner Perkins Caulfield Byers,</i> (2013), <a href="http://www.slideshare.net/kleinerperkins/kpcb-internet-trends-2013">http://www.slideshare.net/kleinerperkins/kpcb-internet-trends-2013</a> .</p>
</div>
<div id="ftn3">
<p><a href="#_ftnref3" name="_ftn3">[3]</a> Douglas, L<i>. </i> <a href="http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf"> <i>"3D Data Management: Controlling Data Volume, Velocity and Variety"</i> </a> <i> . Gartner, </i> (2001)</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4">[4]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a>, Tene, O., &Polonetsky, J. Big Data for All: Privacy and User Control in the Age of Analytics<i>, 11 Nw. J. Tech. &Intell. Prop. 239</i> (2013) <a href="http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1">http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1</a></p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5">[5]</a> Ibid.,</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6">[6]</a> Joh. E, 'Policing by Numbers: Big Data and the Fourth Amendment', <i>Washington Law Review, Vol. 85: 35, </i>(2014) <a href="https://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1319/89WLR0035.pdf?sequence=1"> https://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1319/89WLR0035.pdf?sequence=1 </a></p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7">[7]</a> Raghupathi, W., &Raghupathi, V. <a href="http://www.hissjournal.com/content/2/1/3">Big data analytics in healthcare: promise and potential</a>. <i>Health Information Science and Systems</i>, (2014)</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8">[8]</a> Anderson, R., & Roberts, D. 'Big Data: Strategic Risks and Opportunities, <i>Crowe Horwarth Global Risk Consulting Limited</i>, (2012) <a href="https://www.crowehorwath.net/uploadedfiles/crowe-horwath-global/tabbed_content/big%20data%20strategic%20risks%20and%20opportunities%20white%20paper_risk13905.pdf"> https://www.crowehorwath.net/uploadedfiles/crowe-horwath-global/tabbed_content/big%20data%20strategic%20risks%20and%20opportunities%20white%20paper_risk13905.pdf </a></p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9">[9]</a> Ibid.</p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10">[10]</a> Kshetri. N, 'The Emerging role of Big Data in Key development issues: Opportunities, challenges, and concerns'. <i>Big Data & Society</i> (2014)<a href="http://bds.sagepub.com/content/1/2/2053951714564227.abstract">http://bds.sagepub.com/content/1/2/2053951714564227.abstract</a>,</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11">[11]</a> Tene, O., &Polonetsky, J. Big Data for All: Privacy and User Control in the Age of Analytics<i>, 11 Nw. J. Tech. &Intell. Prop. 239</i> (2013) <a href="http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1">http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1</a></p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12">[12]</a> Cisco, 'IoE-Driven Smart Street Lighting Project Allows Oslo to Reduce Costs, Save Energy, Provide Better Service', Cisco, (2014) Available at: <a href="http://www.cisco.com/c/dam/m/en_us/ioe/public_sector/pdfs/jurisdictions/Oslo_Jurisdiction_Profile_051214REV.pdf"> http://www.cisco.com/c/dam/m/en_us/ioe/public_sector/pdfs/jurisdictions/Oslo_Jurisdiction_Profile_051214REV.pdf </a></p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13">[13]</a> Newell, B, C. Local Law Enforcement Jumps on the Big Data Bandwagon: Automated License Plate Recognition Systems, Information Privacy, and Access to Government Information. <i>University of Washington - the Information School</i>, (2013) <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2341182">http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2341182</a></p>
</div>
<div id="ftn14">
<p><a href="#_ftnref14" name="_ftn14">[14]</a> Morris, D. Big data could improve supply chain efficiency-if companies would let it<i>, Fortune, August 5 </i>2015, http://fortune.com/2015/08/05/big-data-supply-chain/</p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15">[15]</a> Tucker, Darren S., & Wellford, Hill B., Big Mistakes Regarding Big Data, Antitrust Source, American Bar Association, (2014). Available at SSRN: http://ssrn.com/abstract=2549044</p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16">[16]</a> Davenport, T., Barth., Bean, R. How is Big Data Different, <i>MITSloan Management Review, Fall </i>(2012), Available at, <a href="http://sloanreview.mit.edu/article/how-big-data-is-different/">http://sloanreview.mit.edu/article/how-big-data-is-different/</a></p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17">[17]</a> Tucker, Darren S., & Wellford, Hill B., Big Mistakes Regarding Big Data, Antitrust Source, American Bar Association, (2014). Available at SSRN: http://ssrn.com/abstract=2549044</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18">[18]</a> Raghupathi, W., &Raghupathi, V. <a href="http://www.hissjournal.com/content/2/1/3">Big data analytics in healthcare: promise and potential</a>. <i>Health Information Science and Systems</i>, (2014)</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19">[19]</a> Brown, B., Chui, M., Manyika, J. 'Are you Ready for the Era of Big Data?', <i>McKinsey Quarterly,</i> (2011), Available at, <a href="http://www.t-systems.com/solutions/download-mckinsey-quarterly-/1148544_1/blobBinary/Study-McKinsey-Big-data.pdf"> http://www.t-systems.com/solutions/download-mckinsey-quarterly-/1148544_1/blobBinary/Study-McKinsey-Big-data.pdf </a> ; Benady, D., 'Radical transparency will be unlocked by technology and big data', <i>Guardian </i>(2014) Available at: <a href="http://www.theguardian.com/sustainable-business/radical-transparency-unlocked-technology-big-data"> http://www.theguardian.com/sustainable-business/radical-transparency-unlocked-technology-big-data </a></p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20">[20]</a> Ibid.</p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21">[21]</a> Ibid.</p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22">[22]</a> United Nations, A World That Counts: Mobilising the Data Revolution for Sustainable Development, <i> Report prepared at the request of the United Nations Secretary-General,by the Independent Expert Advisory Group on a Data Revolutionfor Sustainable Development. </i> (2014), pg. 18, see also, Hilbert, M. Big Data for Development: From Information- to Knowledge Societies (2013). Available at SSRN: <a href="http://ssrn.com/abstract=2205145">http://ssrn.com/abstract=2205145</a></p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23">[23]</a> Greenleaf, G. Abandon All Hope? <i>Foreword for Issue 37(2) of the UNSW Law Journal on 'Communications Surveillance, Big Data, and the Law'</i> ,(2014) <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2490425">http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2490425##</a><span>, </span>Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i> Vol. 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a></p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24">[24]</a> Tene, O., &Polonetsky, J. Big Data for All: Privacy and User Control in the Age of Analytics, 11 Nw. J. Tech. &Intell. Prop. 239 (2013) <a href="http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1">http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1</a></p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25">[25]</a> Narayanan and Shmatikov quoted in Ibid.,</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26">[26]</a> OECD, Guidelines on the Protection of Privacy and Transborder Flows of Personal Data, The Organization for Economic Co-Operation and Development, (1999); The European Parliament and the Council of the European Union, EU Data Protection Directive, "Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data," (1995)</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27">[27]</a> Barocas, S., &Selbst, A, D., Big Data's Disparate Impact,<i>California Law Review, Vol. 104, </i>(2015). Available at SSRN: <a href="http://ssrn.com/abstract=2477899" target="_blank">http://ssrn.com/abstract=2477899</a></p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28">[28]</a> Article 29 Working Group., Opinion 03/2013 on purpose limitation, <i>Article 29 Data Protection Working Party, </i>(2013) available at: <a href="http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2013/wp203_en.pdf"> http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2013/wp203_en.pdf </a></p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29">[29]</a> Solove, D, J. Privacy Self-Management and the Consent Dilemma, 126 Harv. L. Rev. 1880 (2013), Available at: <a href="http://scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=2093&context=faculty_publications"> http://scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=2093&context=faculty_publications </a></p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30">[30]</a> Brandimarte, L., Acquisti, A., & Loewenstein, G., Misplaced Confidences:</p>
<p>Privacy and the Control Paradox, <i>Ninth Annual Workshop on the Economics of Information Security (WEIS) June 7-8 2010, Harvard University, Cambridge, MA, </i>(2010), available at: <a href="https://fpf.org/wp-content/uploads/2010/07/Misplaced-Confidences-acquisti-FPF.pdf"> https://fpf.org/wp-content/uploads/2010/07/Misplaced-Confidences-acquisti-FPF.pdf </a></p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31">[31]</a> Solove, D, J., Privacy Self-Management and the Consent Dilemma, <i>126 Harv. L. Rev. 1880</i> (2013), Available at: http://scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=2093&context=faculty_publications</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32">[32]</a> Yu, W, E., Data., Privacy and Big Data-Compliance Issues and Considerations, <i>ISACA Journal, Vol. 3 2014 </i>(2014), available at: <a href="http://www.isaca.org/Journal/archives/2014/Volume-3/Pages/Data-Privacy-and-Big-Data-Compliance-Issues-and-Considerations.aspx"> http://www.isaca.org/Journal/archives/2014/Volume-3/Pages/Data-Privacy-and-Big-Data-Compliance-Issues-and-Considerations.aspx </a></p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33">[33]</a> Ramirez, E., Brill, J., Ohlhausen, M., Wright, J., & McSweeny, T., Data Brokers: A Call for Transparency and Accountability, <i>Federal Trade Commission</i> (2014) https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34">[34]</a> Michel Foucault, Discipline and Punish: The Birth of the Prison. Translated by Alan Sheridan, <i>London: Allen Lane, Penguin,</i> (1977)</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35">[35]</a> Marthews, A., & Tucker, C., Government Surveillance and Internet Search Behavior (2015), available at SSRN: http://ssrn.com/abstract=2412564</p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36">[36]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon', Information, Communication & Society, Vol. 15, Issue 5, (2012)</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37">[37]</a> Hirsch, D., That's Unfair! Or is it? Big Data, Discrimination and the FTC's Unfairness Authority, <i>Kentucky Law Journal, Vol. 103</i>, available at: <a href="http://www.kentuckylawjournal.org/wp-content/uploads/2015/02/103KyLJ345.pdf"> http://www.kentuckylawjournal.org/wp-content/uploads/2015/02/103KyLJ345.pdf </a></p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38">[38]</a> Hill, K., How Target Figured Out A Teen Girl Was Pregnant Before Her Father Didhttp://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/</p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39">[39]</a> Anderson, C (2008) "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete", WIRED, June 23 2008, www.wired.com/2008/06/pb-theory/</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40">[40]</a> Ibid.,</p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41">[41]</a> Kitchen, R (2014) Big Data, new epistemologies and paradigm shifts, Big Data & Society, April-June 2014: 1-12</p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42">[42]</a> Boyd D and Crawford K (2012) Critical questions for big data. Information, Communication and Society 15(5): 662-679</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43">[43]</a> Ibid</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44">[44]</a> Lazer, D., Kennedy, R., King, G., &Vespignani, A. " <a href="http://gking.harvard.edu/publications/parable-Google-Flu%c2%a0Traps-Big-Data-Analysis"> The Parable of Google Flu: Traps in Big Data Analysis </a> ." <i>Science 343</i> (2014): 1203-1205. Copy at <a href="http://j.mp/1ii4ETo">http://j.mp/1ii4ETo</a></p>
</div>
<div id="ftn45">
<p><a href="#_ftnref45" name="_ftn45">[45]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a></p>
</div>
<div id="ftn46">
<p><a href="#_ftnref46" name="_ftn46">[46]</a> Leinweber, D. (2007) 'Stupid data miner tricks: overfitting the S&P 500', The Journal of Investing, vol. 16, no. 1, pp. 15-22. <a href="http://m.shookrun.com/documents/stupidmining.pdf">http://m.shookrun.com/documents/stupidmining.pdf</a></p>
</div>
<div id="ftn47">
<p><a href="#_ftnref47" name="_ftn47">[47]</a> Boyd D and Crawford K (2012) Critical questions for big data. Information, Communication and Society 15(5): 662-679</p>
</div>
<div id="ftn48">
<p><a href="#_ftnref48" name="_ftn48">[48]</a> McCue, C., Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, <i>Butterworth-Heinemann,</i> (2014)</p>
</div>
<div id="ftn49">
<p><a href="#_ftnref49" name="_ftn49">[49]</a> De Zwart, M. J., Humphreys, S., & Van Dissel, B. Surveillance, big data and democracy: lessons for Australia from the US and UK. <i>Http://www.unswlawjournal.unsw.edu.au/issue/volume-37-No-2</i>. (2014) Retrieved from https://digital.library.adelaide.edu.au/dspace/handle/2440/90048</p>
</div>
<div id="ftn50">
<p><a href="#_ftnref50" name="_ftn50">[50]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a>; Newman, N., Search, Antitrust and the Economics of the Control of User Data, <i>31 YALE J. ON REG. 401 </i>(2014)</p>
</div>
<div id="ftn51">
<p><a href="#_ftnref51" name="_ftn51">[51]</a> Newman, N., The Cost of Lost Privacy: Search, Antitrust and the Economics of the Control of User Data (2013). Available at SSRN: http://ssrn.com/abstract=2265026, Newman, N. ,Search, Antitrust and the Economics of the Control of User Data, <i>31 YALE J. ON REG. 401</i> (2014)</p>
</div>
<div id="ftn52">
<p><a href="#_ftnref52" name="_ftn52">[52]</a> Ibid.,</p>
</div>
<div id="ftn53">
<p><a href="#_ftnref53" name="_ftn53">[53]</a> European Data Protection Supervisor, Privacy and competitiveness in the age of big data:</p>
<p>The interplay between data protection, competition law and consumer protection in the Digital Economy, (2014), available at: <a href="https://secure.edps.europa.eu/EDPSWEB/webdav/shared/Documents/Consultation/Opinions/2014/14-03-26_competitition_law_big_data_EN.pdf"> https://secure.edps.europa.eu/EDPSWEB/webdav/shared/Documents/Consultation/Opinions/2014/14-03-26_competitition_law_big_data_EN.pdf </a></p>
</div>
<div id="ftn54">
<p><a href="#_ftnref54" name="_ftn54">[54]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a></p>
</div>
<div id="ftn55">
<p><a href="#_ftnref55" name="_ftn55">[55]</a> Schradie, J., Big Data Not Big Enough? How the Digital Divide Leaves People Out, MediaShift, 31 July 2013, (2013), available at: <a href="http://mediashift.org/2013/07/big-data-not-big-enough-how-digital-divide-leaves-people-out/"> http://mediashift.org/2013/07/big-data-not-big-enough-how-digital-divide-leaves-people-out/ </a></p>
</div>
<div id="ftn56">
<p><a href="#_ftnref56" name="_ftn56">[56]</a> Crawford, K., The Hidden Biases in Big Data, <i>Harvard Business Review, 1 April 2013 </i>(2013), available at: <a href="https://hbr.org/2013/04/the-hidden-biases-in-big-data">https://hbr.org/2013/04/the-hidden-biases-in-big-data</a></p>
</div>
<div id="ftn57">
<p><a href="#_ftnref57" name="_ftn57">[57]</a> Robinson, D., Yu, H., Civil Rights, Big Data, and Our Algorithmic Future, (2014) <a href="http://bigdata.fairness.io/introduction/">http://bigdata.fairness.io/introduction/</a></p>
</div>
<div id="ftn58">
<p><a href="#_ftnref58" name="_ftn58">[58]</a> Ibid.</p>
</div>
<div id="ftn59">
<p><a href="#_ftnref59" name="_ftn59">[59]</a> Ibid</p>
</div>
<div id="ftn60">
<p><a href="#_ftnref60" name="_ftn60">[60]</a> Rotellla, P., Is Data The New Oil? Forbes, 2 April 2012, (2012), available at: <a href="http://www.forbes.com/sites/perryrotella/2012/04/02/is-data-the-new-oil/"> http://www.forbes.com/sites/perryrotella/2012/04/02/is-data-the-new-oil/ </a></p>
</div>
<div id="ftn61">
<p><a href="#_ftnref61" name="_ftn61">[61]</a> Barocas, S., &Selbst, A, D., Big Data's Disparate Impact,<i>California Law Review, Vol. 104, </i>(2015). Available at SSRN: <a href="http://ssrn.com/abstract=2477899" target="_blank">http://ssrn.com/abstract=2477899</a>; Kshetri. N, 'The Emerging role of Big Data in Key development issues: Opportunities, challenges, and concerns'. <i>Big Data & Society</i>(2014) <a href="http://bds.sagepub.com/content/1/2/2053951714564227.abstract">http://bds.sagepub.com/content/1/2/2053951714564227.abstract</a></p>
</div>
<div id="ftn62">
<p><a href="#_ftnref62" name="_ftn62">[62]</a> Pasquale, F., The Black Box Society: The Secret Algorithms That Control Money and Information, Harvard University Press , (2015)</p>
</div>
</div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/benefits-and-harms-of-big-data'>http://editors.cis-india.org/internet-governance/blog/benefits-and-harms-of-big-data</a>
</p>
No publisherScott MasonInternet GovernanceBig Data2015-12-30T02:48:08ZBlog EntryBig Data and Governance in India
http://editors.cis-india.org/internet-governance/events/big-data-governance-india
<b>The Centre for Internet & Society (CIS) is happy to invite you to a discussion on the role of Big Data in governance in India with a focus on Digital India, UID Scheme and Smart Cities Mission in India on January 23, 2016 at CIS office in Bangalore from 11 a.m. to 4 p.m.</b>
<h3><a href="http://editors.cis-india.org/internet-governance/blog/background-note-big-data" class="internal-link">Background Note</a></h3>
<hr />
<p>The roundtable discussion intends to delve deeper into various issues around the role of big data in Government schemes and projects like the Digital India, the UID Scheme and the 100 Smart Cities Mission. Some of the topics would include:</p>
<ul>
<li>Use/Assumptions about use of Big Data.</li>
<li>The public dialogue in the context of Big Data, rights, and governance.</li>
<li>Status and Role of India's data protection standards impacted by Big Data.</li>
<li>Legal hurdles posed by Big Data.</li>
</ul>
<p>We look forward to making this a forum for knowledge exchange and a learning opportunity for our friends and colleagues attending the discussion.</p>
<p><b>Contact:</b></p>
<ul>
<li>Vanya Rakesh vanya@cis-india.org +919586572707</li>
<li>Amber Sinha amber@cis-india.org +919620180343</li>
</ul>
<h2>Agenda</h2>
<table class="plain">
<tbody>
<tr>
<td>Introduction<br />11:00 am - 11.30 am<br /><br /></td>
<td>Introduction about “Big Data in the Global South: Mitigating Harms” and “Big Data in Indian Governance”.</td>
</tr>
<tr>
<td>Digital India<br />11.30 am - 1:00 pm<br /><br /></td>
<td>Discussion<br /><br />
<ul>
<li>Schemes under Digital India and how Big Data pertains to them</li>
</ul>
<ul>
<li>Scale and nature of data being collected</li>
</ul>
<ul>
<li>Actors involved</li>
</ul>
<ul>
<li>Research Methodology and coding</li>
</ul>
<ul>
<li>“Cradle to grave” identity</li>
</ul>
<ul>
<li>Need for privacy legislation/data protection policies</li>
</ul>
</td>
</tr>
<tr>
<td>1:00 pm- 2:00 pm <br /></td>
<td>Lunch</td>
</tr>
<tr>
<td>Big Data and Smart Cities<br />2:00 pm - 3:30pm <br /><br /></td>
<td>Discussion<br /><br />
<ul>
<li>Use/Assumptions about use of Big Data in Smart cities.</li>
</ul>
<ul>
<li>Organisations/companies driving the use of Big Data in Governance in India</li>
</ul>
<ul>
<li>The public dialogue around the scheme in the context of big data, rights, and governance</li>
</ul>
<ul>
<li>Impact of Big Data on India's Data Protection Standards </li>
</ul>
<ul>
<li>Impact of Big Data on other legislation/policy besides privacy . What type of 'legal hurdles' could Big Data pose?</li>
</ul>
<ul>
<li>Need for creating regulatory/legal framework</li>
</ul>
</td>
</tr>
<tr>
<td>3:30pm-4:00pm</td>
<td>Tea/Coffee</td>
</tr>
</tbody>
</table>
<ul>
</ul>
<h2>Detailed Agenda</h2>
<h3>Digital India</h3>
<p><b>Scope of schemes under Digital India and how Big Data pertains to them</b></p>
<ul>
<li>What are the ways in which Big Data is defined?</li>
<li>What aspects of Digital India initiatives pertain to Big Data?</li>
<li>What could be the harms/benefits of Big Data for Digital India?</li>
</ul>
<p><b>Scale and nature of data being collected</b></p>
<ul>
<li>What do the schemes intend to quantify?</li>
</ul>
<p><b>Actors involved</b></p>
<ul>
<li>What kinds of issue arise in PPP model?</li>
<li>Questions about ownership of data, access-control and security</li>
<li>Application of Section 43A rules to private parties involved</li>
</ul>
<p><b>Research Methodology and coding</b></p>
<ul>
<li>What the relevant questions that need to be asked in mapping each scheme?</li>
<li>How do we view e-governance initiatives vis-a-vis privacy principles?</li>
<li>What are the rights of citizens, and how are they impacted?</li>
</ul>
<p><b>“Cradle to grave” identity</b></p>
<ul>
<li>What does ‘cradle to grave’ digital identity mean?</li>
<li>What is the impact of using the Aadhaar number?</li>
</ul>
<p><b>Need for privacy legislation/data protection policies</b></p>
<ul>
<li>What aspects of the right to privacy pertain to the schemes?</li>
<li>Extending the Section 43A rules to government agencies</li>
<li>Justice Shah committee’s nine privacy principles.</li>
</ul>
<h3>Big Data and Smart Cities</h3>
<p><b>Use/Assumptions about use of Big Data in Smart cities</b></p>
<ul>
<li>What can be termed as big data in the context of smart cities.</li>
<li>What would be the role of big data.</li>
<li>Where do we see use/potential use of big data in the smart cities.</li>
</ul>
<p><b>What bodies/companies are driving the use of Big Data in Governance in India? </b></p>
<ul>
<li>Identifying actors involved.</li>
<li>Defining the role of: Government bodies, Private companies like IT Companies, consultants, etc. in use of big data. Clarity on ownership, storage, use, re-use, deletion of data. Question of accountability in case of breach/misuse.</li>
</ul>
<p><b>What has been the public dialogue around a scheme in the context of big data, rights, and governance? </b></p>
<ul>
<li>Weighing promises of big data.</li>
<li>Weighing challenges of big data.</li>
<li>Concerns around big data- data security, privacy, digital resilience of infrastructure, risks of identity management, Circumvention of democracy, social exclusion, right to equality, right to access, etc.</li>
<li>Issue of governance and implementation: role of SPVs.</li>
</ul>
<p><b>How are India's data protection standards impacted by Big Data? </b></p>
<ul>
<li>Need for developing standards.</li>
<li>Drawing from existing international standards.</li>
</ul>
<p><b>Are there other legislation/policy besides privacy impacted by Big Data? what type of 'legal hurdles' could Big Data pose?</b></p>
<ul>
<li>Legal landscaping: impact on current laws/policies/provisions.</li>
</ul>
<p><b>Need for creating regulatory/legal framework?</b></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/events/big-data-governance-india'>http://editors.cis-india.org/internet-governance/events/big-data-governance-india</a>
</p>
No publisherpraskrishnaBig DataPrivacyInternet GovernanceSmart CitiesEvent2016-01-17T01:57:45ZEventBig Data in the Global South - An Analysis
http://editors.cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis
<b></b>
<h3 style="text-align: justify; "><b>I. </b> <b>Introduction</b></h3>
<p style="text-align: justify; ">"<i>The period that we have embarked upon is unprecedented in history in terms of our ability to learn about human behavior.</i>" <a href="#_ftn1" name="_ftnref1">[1]</a></p>
<p style="text-align: justify; ">The world we live in today is facing a slow but deliberate metamorphosis of decisive information; from the erstwhile monopoly of world leaders and the captains of industry obtained through regulated means, it has transformed into a relatively undervalued currency of knowledge collected from individual digital expressions over a vast network of interconnected electrical impulses.<a href="#_ftn2" name="_ftnref2">[2]</a> This seemingly random deluge of binary numbers, when interpreted represents an intricately woven tapestry of the choices that define everyday life, made over virtual platforms. The machines we once employed for menial tasks have become sensorial observers of our desires, wants and needs, so much so that they might now predict the course of our future choices and decisions.<a href="#_ftn3" name="_ftnref3">[3]</a> The patterns of human behaviour that are reflected within this data inform policy makers, in both a public and private context. The collective data obtained from our digital shadows thus forms a rapidly expanding storehouse of memory, from which interested parties can draw upon to resolve problems and enable a more efficient functioning of foundational institutions, such as the markets, the regulators and the government.<a href="#_ftn4" name="_ftnref4">[4]</a></p>
<p style="text-align: justify; ">The term used to describe a large volume of collected data, in a structured as well as unstructured form is called Big Data. This data requires niche technology, outside of traditional software databases, to process; simply because of its exponential increment in a relatively short period of time. Big Data is usually identified using a "three V" characterization - larger volume, greater variety and distinguishably high rates of velocity. <a href="#_ftn5" name="_ftnref5">[5]</a> This is exemplified in the diverse sources from which this data is obtained; mobile phone records, climate sensors, social media content, GPS satellite identifications and patterns of employment, to name a few. Big data analytics refers to the tools and methodologies that aim to transform large quantities of raw data into "interpretable data", in order to study and discern the same so that causal relationships between events can be conclusively established.<a href="#_ftn6" name="_ftnref6">[6]</a> Such analysis could allow for the encouragement of the positive effects of such data and a concentrated mitigation of negative outcomes.</p>
<p style="text-align: justify; ">This paper seeks to map out the practices of different governments, civil society, and the private sector with respect to the collection, interpretation and analysis of big data in the global south, illustrated across a background of significant events surrounding the use of big data in relevant contexts. This will be combined with an articulation of potential opportunities to use big data analytics within both the public and private spheres and an identification of the contextual challenges that may obstruct the efficient use of this data. The objective of this study is to deliberate upon how significant obstructions to the achievement of developmental goals within the global south can be overcome through an accurate recognition, interpretation and analysis of big data collected from diverse sources.<b> </b></p>
<h3 style="text-align: justify; "><b>II. </b> <b>Uses of Big Data in the Global Development</b></h3>
<p style="text-align: justify; ">Big Data for development is the process though which raw, unstructured and imperfect data is analyzed, interpreted and transformed into information that can be acted upon by governments and policy makers in various capacities. The amount of digital data available in the world today has grown from 150 exabytes in 2005 to 1200 exabytes in 2010.<a href="#_ftn7" name="_ftnref7">[7]</a> It is predicted that this figure would increase by 40% annually in the next few years<a href="#_ftn8" name="_ftnref8">[8]</a>, which is close to 40 times growth of the world's population. <a href="#_ftn9" name="_ftnref9">[9]</a> The implication of this is essentially that the share of available data in the world today that is less than a minute old is increasing at an exponential rate. Moreover, an increasing percentage of this data is produced and created real-time.</p>
<p style="text-align: justify; ">The data revolution that is incumbent upon us is characterized by a rapidly accumulating and continuously evolving stock of data prevalent` in both industrialized as well as developing countries. This data is extracted from technological services that act as sensors and reflect the behaviour of individuals in relation to their socio-economic circumstances.</p>
<p style="text-align: justify; ">For many global south countries, this data is generated through mobile phone technology. This trend is evident in Sub Saharan Africa, where mobile phone technology has been used as an effective substitute for often weak and unstructured State mechanisms such as faulty infrastructure, underdeveloped systems of banking and inferior telecommunication networks.<a href="#_ftn10" name="_ftnref10">[10]</a></p>
<p style="text-align: justify; ">For example, a recent study presented at the Data for Development session at the NetMob Conference at MIT used mobile phone data to analyze the impact of opening a new toll highway in Dakar, Senegal on human mobility, particularly how people commute to work in the metropolitan area. <a href="#_ftn11" name="_ftnref11"><sup><sup>[11]</sup></sup></a> A huge investment, the improved infrastructure is expected to result in a significant increase of people in and out of Dakar, along with the transport of essential goods. This would initiate rural development in the areas outside of Dakar and boost the value of land within the region.<a href="#_ftn12" name="_ftnref12"><sup><sup>[12]</sup></sup></a> The impact of the newly constructed highway can however only be analyzed effectively and accurately through the collection of this mobile phone data from actual commuters, on a real time basis.</p>
<p style="text-align: justify; ">Mobile phones technology is no longer used just for personal communication but has been transformed into an effective tool to secure employment opportunities, transfer money, determine stock options and assess the prices of various commodities.<a href="#_ftn13" name="_ftnref13">[13]</a> This generates vast amounts of data about individuals and their interactions with the government and private sector companies. Internet Traffic is predicted to grow between 25 to 30 % in the next few years in North America, Western Europe and Japan but in Latin America, The Middle East and Africa this figure has been expected to touch close to 50%.<a href="#_ftn14" name="_ftnref14">[14]</a> The bulk of this internet traffic can be traced back to mobile devices.</p>
<p style="text-align: justify; ">The potential applicability of Big Data for development at the most general level is the ability to provide an overview of the well being of a given population at a particular period of time.<a href="#_ftn15" name="_ftnref15">[15]</a> This overcomes the relatively longer time lag that is prevalent with most other traditional forms of data collection. The analysis of this data has helped, to a large extent, uncover "digital smoke signals" - or inherent changes in the usage patterns of technological services, by individuals within communities.<a href="#_ftn16" name="_ftnref16">[16]</a> This may act as an indicator of the changes in the underlying well-being of the community as a whole. This information about the well-being of a community derived from their usage of technology provides significantly relevant feedback to policy makers on the success or failure of particular schemes and can pin point changes that need to be made to status quo. <a href="#_ftn17" name="_ftnref17">[17]</a>The hope is that this feedback delivered in real-time, would in turn lead to a more flexible and accessible system of international development, thus securing more measurable and sustained outcomes. <a href="#_ftn18" name="_ftnref18">[18]</a></p>
<p style="text-align: justify; ">The analysis of big data involves the use of advanced computational technology that can aid in the determination of trends, patterns and correlations within unstructured data so as to transform it into actionable information. It is hoped that this in addition to the human perspective and experience afforded to the process could enable decision makers to rely upon information that is both reliable and up to date to formulate durable and self-sustaining development policies.</p>
<p style="text-align: justify; ">The availability of raw data has to be adequately complemented with intent and a capacity to use it effectively. To this effect, there is an emerging volume of literature that seeks to characterize the primary sources of this Big Data as sharing certain easily distinguishable features. Firstly, it is digitally generated and can be stored in a binary format, thus making it susceptible to requisite manipulation by computers attempting to engage in its interpretation. It is passively produced as a by-product of digital interaction and can be automatically extracted for the purpose of continuous analysis. It is also geographically traceable within a predetermined time period. It is however important to note that "real time" does not necessarily refer to information occurring instantly but is reflective of the relatively short time in which the information is produced and made available thus making it relevant within the requisite timeframe. This allows efficient responsive action to be taken in a short span of time thus creating a feedback loop. <a href="#_ftn19" name="_ftnref19">[19]</a></p>
<p style="text-align: justify; ">In most cases the granularity of the data is preferably sought to be expanded over a larger spatial context such as a village or a community as opposed to an individual simply because this affords an adequate recognition of privacy concerns and the lack of definitive consent of the individuals in the extraction of this data. In order to ease the process of determination of this data, the UN Global Pulse has developed taxonomy of sorts to assess the types of data sources that are relevant to utilizing this information for development purposes.<a href="#_ftn20" name="_ftnref20">[20]</a> These include the following sources;</p>
<p style="text-align: justify; "><i>Data Exhaust</i> or the digital footprint left behind by individuals' use of technology for service oriented tasks such as web purchases, mobile phone transactions and real time information collected by UN agencies to monitor their projects such as levels of food grains in storage units, attendance in schools etc.</p>
<p style="text-align: justify; "><i>Online Information</i> which includes user generated content on the internet such as news, blog entries and social media interactions which may be used to identify trends in human desires, perceptions and needs.</p>
<p style="text-align: justify; "><i>Physical sensors</i> such as satellite or infrared imagery of infrastructural development, traffic patterns, light emissions and topographical changes, thus enabling the remote sensing of changes in human activity over a period of time.</p>
<p style="text-align: justify; "><i>Citizen reporting or crowd sourced data</i> , which includes information produced on hotlines, mobile based surveys, customer generated maps etc. Although a passive source of data collection, this is a key instrument in assessing the efficacy of action oriented plans taken by decision makers.</p>
<p style="text-align: justify; ">The capacity to analyze this big data is hinged upon the reliance placed on technologically advanced processes such as powerful algorithms which can synthesize the abundance of raw data and break down the information enabling the identification of patterns and correlations. This process would rely on advanced visualization techniques such <i>"sense-making tools"<a href="#_ftn21" name="_ftnref21"><b>[21]</b></a></i></p>
<p style="text-align: justify; ">The identification of patterns within this data is carried out through a process of instituting a common framework for the analysis of this data. This requires the creation of a specific lexicon that would help tag and sort the collected data. This lexicon would specify <i>what </i>type of information is collected and <i>who </i>it is interpreted and collected by, the observer or the reporter. It would also aid in the determination of <i>how </i>the data is acquired and the qualitative and quantitative nature of the data. Finally, the spatial context of the data and the time frame within which it was collected constituting the aspects of <i>where </i>and <i>when</i> would be taken into consideration. The data would then be analyzed through a process of <i>Filtering, Summarizing and Categorizing</i> the data by transforming it into an appropriate collection of relevant indicators of a particular population demographic. <a href="#_ftn22" name="_ftnref22">[22]</a></p>
<p style="text-align: justify; ">The intensive mining of predominantly socioeconomic data is known as "reality mining" <a href="#_ftn23" name="_ftnref23">[23]</a> and this can shed light on the processes and interactions that are reflected within the data. This is carried out via a tested three fold process. Firstly, the " <i>Continuous Analysis over the streaming of the data", </i>which involves the monitoring and analyzing high frequency data streams to extract often uncertain raw data. For example, the systematic gathering of the prices of products sold online over a period of time. Secondly, <i>"The Online digestion of semi structured data and unstructured data", </i>which includes news articles, reviews of services and products and opinion polls on social media that aid in the determination of public perception, trends and contemporary events that are generating interest across the globe. Thirdly, a <i>'Real-time Correlation of streaming data with slowly accessible historical data repositories,' </i>which refers to the "mechanisms used for correlating and integrating data in real-time with historical records."<a href="#_ftn24" name="_ftnref24">[24]</a> The purpose of this stage is to derive a contextualized perception of personalized information that seeks to add value to the data by providing a historical context to it. <i> </i>Big Data for development purposes would make use of a combination of these depending on the context and need.<b> </b></p>
<p style="text-align: justify; "><b>(i) </b> <b>Policy Formulation </b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">The world today has become increasingly volatile in terms of how the decisions of certain countries are beginning to have an impact on vulnerable communities within entirely different nations. Our global economy has become infinitely more susceptible to fluctuating conditions primarily because of its interconnectivity hinged upon transnational interdependence. The primordial instigators of most of these changes, including the nature of harvests, prices of essential commodities, employment structures and capital flows, have been financial and environmental disruptions. <a href="#_ftn25" name="_ftnref25">[25]</a> According to the OECD, " <i> Disruptive shocks to the global economy are likely to become more frequent and cause greater economic and social hardship. The economic spillover effects of events like the financial crisis or a potential pandemic will grow due to the increasing interconnectivity of the global economy and the speed with which people, goods and data travel."<a href="#_ftn26" name="_ftnref26"><b>[26]</b></a> </i></p>
<p style="text-align: justify; ">The local impacts of these fluctuations may not be easily visible or even traceable but could very well be severe and long lasting. A vibrant literature on the vulnerability of communities has highlighted the impacts of these shocks on communities often causing children to drop out of school, families to sell their productive assets, and communities to place a greater reliance on state rations.<a href="#_ftn27" name="_ftnref27">[27]</a> These vulnerabilities cannot be definitively discerned through traditional systems of monitoring and information collection. The evidence of the effects of these shocks often take too long to reach decision makers; who are unable to formulate effective policies without ascertaining the nature and extent of the hardships suffered by these in a given context. The existing early warning systems in place do help raise flags and draw attention to the problem but their reach is limited and veracity compromised due to the time it takes to extract and collate this information through traditional means. These traditional systems of information collection are difficult to implement within rural impoverished areas and the data collected is not always reliable due to the significant time gap in its collection and subsequent interpretation. Data collected from surveys does provide an insight into the state of affairs of communities across demographics but this requires time to be collected, processed, verified and eventually published. Further, the expenses incurred in this process often prove to be difficult to offset.</p>
<p style="text-align: justify; "><b> The digital revolution therefore provides a significant opportunity to gain a richer and deeper insight into the very nature and evolution of the human experience itself thus affording a more legitimate platform upon which policy deliberations can be articulated. This data driven decision making, once the monopoly of private institutions such as The World Economic Forum and The McKinsey Institute <a href="#_ftn28" name="_ftnref28"><b>[28]</b></a> has now emerged at the forefront of the public policy discourse. Civil society has also expressed an eagerness to be more actively involved in the collection of real-time data after having perceived its benefits. This is evidenced by the emergence of 'crowd sourcing'<a href="#_ftn29" name="_ftnref29"><b>[29]</b></a> and other 'participatory sensing' <a href="#_ftn30" name="_ftnref30"><b>[30]</b></a> efforts that are founded upon the commonalities shared by like minded communities of individuals. This is being done on easily accessible platforms such as mobile phone interfaces, hand-held radio devices and geospatial technologies. <a href="#_ftn31" name="_ftnref31"><b>[31]</b></a> </b></p>
<p style="text-align: justify; ">The predictive nature of patterns identifiable from big data is extremely relevant for the purpose of developing socio-economic policies that seek to bridge problem-solution gaps and create a conducive environment for growth and development. Mobile phone technology has been able to quantify human behavior on an unprecedented scale.<a href="#_ftn32" name="_ftnref32">[32]</a> This includes being able to detect changes in standard commuting patterns of individuals based on their employment status<a href="#_ftn33" name="_ftnref33">[33]</a> and estimating a country's GDP in real-time by measuring the nature and extent of light emissions through remote sensing. <a href="#_ftn34" name="_ftnref34">[34]</a></p>
<p style="text-align: justify; ">A recent research study has concluded that "due to the relative frequency of certain queries being highly correlated with the percentage of physician visits in which individuals present influenza symptoms, it has been possible to accurately estimate the levels of influenza activity in each region of the United States, with a reporting lag of just a day." Online data has thus been used as a part of syndromic surveillance efforts also known as infodemiology. <a href="#_ftn35" name="_ftnref35">[35]</a> The US Centre for Disease Control has concluded that mining vast quantities of data through online health related queries can help detect disease outbreaks " <i> before they have been confirmed through a diagnosis or a laboratory confirmation." <a href="#_ftn36" name="_ftnref36"><b>[36]</b></a> </i> Google trends works in a similar way.</p>
<p style="text-align: justify; ">Another public health monitoring system known as the Healthmap project compiles seemingly fragmented data from news articles, social media, eye-witness reports and expert discussions based on validated studies to "<i>achieve a unified and comprehensive view of the current global state of infectious diseases"</i> that may be visualized on a map. <a href="#_ftn37" name="_ftnref37">[37]</a></p>
<p style="text-align: justify; ">Big Data used for development purpose can reduce the reliance on human inputs thus narrowing the room for error and ensuring the accuracy of information collected upon which policy makers can base their decisions.<b> </b></p>
<p style="text-align: justify; "><b>(ii) </b> <b>Advocacy and Social Change</b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Due to the ability of Big Data to provide an unprecedented depth of detail on particular issues, it has often been used as a vehicle of advocacy to highlight various issues in great detail. This makes it possible to ensure that citizens are provided with a far more participative experience, capturing their attention and hence better communicating these problems. Numerous websites have been able to use this method of crowd sourcing to broadcast socially relevant issues<a href="#_ftn38" name="_ftnref38">[38]</a>. Moreover, the massive increase in access to the internet has dramatically improved the scope for activism through the use of volunteered data due to which advocates can now collect data from volunteers more effectively and present these issues in various forums. Websites like Ushahidi<a href="#_ftn39" name="_ftnref39">[39]</a> and the Black Monday Movement <a href="#_ftn40" name="_ftnref40">[40]</a> being prime examples of the same. These platforms have championed various causes, consistently exposing significant social crises' that would otherwise go unnoticed.</p>
<p style="text-align: justify; ">The Ushahidi application used crowd sourcing mechanisms in the aftermath of the Haiti earthquake to set up a centralized messaging system that allowed mobile phone users to provide information on injured and trapped people.<a href="#_ftn41" name="_ftnref41">[41]</a> An analysis of the data showed that the concentration of text messages was correlated with the areas where there was an increased concentration of damaged buildings. <a href="#_ftn42" name="_ftnref42">[42]</a> Patrick Meier of Ushahidi noted "These results were evidence of the system's ability to predict, with surprising accuracy and statistical significance, the location and extent of structural damage post the earthquake." <a href="#_ftn43" name="_ftnref43">[43]</a></p>
<p style="text-align: justify; ">Another problem that data advocacy hopes to tackle, however, is that of too much exposure, with advocates providing information to various parties to help ensure that there exists no unwarranted digital surveillance and that sensitive advocacy tools and information are not used inappropriately. An interesting illustration of the same is The Tactical Technology Collective<a href="#_ftn44" name="_ftnref44">[44]</a> that hopes to improve the use of technology by activists and various other political actors. The organization, through various mediums such as films, events etc. hopes to train activists regarding data protection and privacy awareness and skills among human rights activists. Additionally, Tactical Technology also assists in ensuring that information is used in an appealing and relevant manner by human rights activists and in the field of capacity building for the purposes of data advocacy.</p>
<p style="text-align: justify; ">Observed data such as mobile phone records generated through network operators as well as through the use of social media are beginning to embody an omnipotent role in the development of academia through detailed research. This is due to the ability of this data to provide microcosms of information within both contexts of finer granularity and over larger public spaces. In the wake of natural disasters, this can be extremely useful, as reflected by the work of Flowminder after the 2010 Haiti earthquake.<a href="#_ftn45" name="_ftnref45">[45]</a> A similar string of interpretive analysis can be carried out in instances of conflict and crises over varying spans of time. Flowminder used the geospatial locations of 1.9 million subscriber identity modules in Haiti, beginning 42 days before the earthquake and 158 days after it. This information allowed researches to empirically determine the migration patterns of population post the earthquake and enabled a subsequent UNFPA household survey.<a href="#_ftn46" name="_ftnref46">[46]</a> In a similar capacity, the UN Global Pulse is seeking to assist in the process of consultation and deliberation on the specific targets of the millennium development goals through a framework of visual analytics that represent the big data procured on each of the topics proposed for the post- 2015 agenda online.<a href="#_ftn47" name="_ftnref47">[47]</a></p>
<p style="text-align: justify; ">A recent announcement of collaboration between RTI International, a non-profit research organization and IBM research lab looks promising in its initiative to utilize big data analytics in schools within Mombasa County, Kenya.<a href="#_ftn48" name="_ftnref48">[48]</a> The partnership seeks to develop testing systems that would capture data that would assist governments, non-profit organizations and private enterprises in making more informed decisions regarding the development of education and human resources within the region. Äs observed by Dr. Kamal Bhattacharya, The Vice President of IBM Research, "A significant lack of data on Africa in the past has led to misunderstandings regarding the history, economic performance and potential of the government." The project seeks to improve transparency and accountability within the schooling system in more than 100 institutions across the county. The teachers would be equipped with tablet devices to collate the data about students, classrooms and resources. This would allow an analysis of the correlation between the three aspects thus enabling better policy formulation and a more focused approach to bettering the school system. <a href="#_ftn49" name="_ftnref49">[49]</a> This is a part of the United States Agency for International Development's Education Data for Decision Making (EdData II) project. According to Dr Kommy Weldemariam, Research Scientist , IBM Research, "… there has been a significant struggle in making informed decisions as to how to invest in and improve the quality and content of education within Sub-Saharan Africa. The Project would create a school census hub which would enable the collection of accurate data regarding performance, attendance and resources at schools. This would provide valuable insight into the building of childhood development programs that would significantly impact the development of an efficient human capital pool in the near future."<a href="#_ftn50" name="_ftnref50">[50]</a></p>
<p style="text-align: justify; ">A similar initiative has been undertaken by Apple and IBM in the development of the "Student Achievement App" which seeks to use this data for "content analysis of student learning". The Application as a teaching tool that analyses the data provided to develop actionable intelligence on a per-student basis." <a href="#_ftn51" name="_ftnref51">[51]</a> This would give educators a deeper understanding of the outcome of teaching methodologies and subsequently enable better leaning. The impact of this would be a significant restructuring of how education is delivered. At a recent IBM sponsored workshop on education held in India last year , Katharine Frase, IBM CTO of Public Sector predicted that "classrooms will look significantly different within a decade than they have looked over the last 200 years."<a href="#_ftn52" name="_ftnref52">[52]</a><b> </b></p>
<p style="text-align: justify; "><b>(iii) </b> <b>Access and the exchange of information </b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Big data used for development serves as an important information intermediary that allows for the creation of a unified space within which unstructured heterogeneous data can be efficiently organized to create a collaborative system of information. New interactive platforms enable the process of information exchange though an internal vetting and curation that ensures accessibility to reliable and accurate information. This encourages active citizen participation in the articulation of demands from the government, thus enabling the actualization of the role of the electorate in determining specific policy decisions.</p>
<p style="text-align: justify; ">The Grameen Foundation's AppLab in Kampala aids in the development of tools that can use the information from micro financing transactions of clients to identify financial plans and instruments that would be be more suitable to their needs.<a href="#_ftn53" name="_ftnref53">[53]</a> Thus, through working within a community, this technology connects its clients in a web of information sharing that they both contribute to and access after the source of the information has been made anonymous. This allows the individual members of the community to benefit from this common pool of knowledge. The AppLab was able to identify the emergence of a new crop pest from an increase in online searches for an unusual string of search terms within a particular region. Using this as an early warning signal, the Grameen bank sent extension officers to the location to check the crops and the pest contamination was dealt with effectively before it could spread any further.<a href="#_ftn54" name="_ftnref54">[54]</a><b> </b></p>
<p style="text-align: justify; "><b>(iv) </b> <b>Accountability and Transparency</b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Big data enables participatory contributions from the electorate in existing functions such as budgeting and communication thus enabling connections between the citizens, the power brokers and elites. The extraction of information and increasing transparency around data networks is also integral to building a self-sustaining system of data collection and analysis. However it is important to note that this information collected must be duly analyzed in a responsible manner. Checking the veracity of the information collected and facilitating individual accountability would encourage more enthusiastic responses from the general populous thus creating a conducive environment to elicit the requisite information. The effectiveness of the policies formulated by relying on this information would rest on the accuracy of such information.</p>
<p style="text-align: justify; ">An example of this is Chequeado, a non-profit Argentinean media outlet that specializes in fact-checking. It works on a model of crowd sourcing information on the basis of which it has fact checked everything from the live presidential speech to congressional debates that have been made open to the public. <a href="#_ftn55" name="_ftnref55">[55]</a> It established a user friendly public database, DatoCHQ, in 2014 which allowed its followers to participate in live fact-checks by sending in data, which included references, facts, articles and questions, through twitter. <a href="#_ftn56" name="_ftnref56">[56]</a> This allowed citizens to corroborate the promises made by their leaders and instilled a sense of trust in the government.<b> </b></p>
<h3 style="text-align: justify; "><b>III. </b> <b>Big Data and Smart Cities in the Global South </b></h3>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Smart cities have become a buzzword in South Asia, especially after the Indian government led by Prime Minister Narendra Modi made a commitment to build 100 smart cities in India<a href="#_ftn57" name="_ftnref57">[57]</a>. A smart city is essentially designed as a hub where the information and communication technologies (ICT) are used to create feedback loops with an almost minimum time gap. In traditional contexts, surveys carried out through a state sponsored census were the only source of systematic data collection. However these surveys are long drawn out processes that often result in a drain on State resources. Additionally, the information obtained is not always accurate and policy makers are often hesitant to base their decisions on this information. The collection of data can however be extremely useful in improving the functionality of the city in terms of both the 'hard' or physical aspects of the infrastructural environment as well as the 'soft' services it provides to citizens. One model of enabling this data collection, to this effect, is a centrally structured framework of sensors that may be able to determine movements and behaviors in real-time, from which the data obtained can be subsequently analyzed. For example, sensors placed under parking spaces at intersections can relay such information in short spans of time. South Korea has managed to implement a similar structure within its smart city, Songdo.<a href="#_ftn58" name="_ftnref58">[58]</a></p>
<p style="text-align: justify; ">Another approach to this smart city model is using crowd sourced information through apps, either developed by volunteers or private conglomerates. These allow for the resolving of specific problems by organizing raw data into sets of information that are attuned to the needs of the public in a cohesive manner. However, this system would require a highly structured format of data sets, without which significantly transformational result would be difficult to achieve.<a href="#_ftn59" name="_ftnref59">[59]</a></p>
<p style="text-align: justify; ">There does however exist a middle ground, which allows the beneficiaries of this network, the citizens, to take on the role of primary sensors of information. This method is both cost effective and allows for an experimentation process within which an appropriate measure of the success or failure of the model would be discernible in a timely manner. It is especially relevant in fast growing cities that suffer congestion and breakdown of infrastructure due to the unprecedented population growth. This population is now afforded with the opportunity to become a part of the solution.</p>
<p style="text-align: justify; ">The principle challenge associated with extracting this Big Data is its restricted access. Most organizations that are able to collect this big data efficiently are private conglomerates and business enterprises, who use this data to give themselves a competitive edge in the market, by being able to efficiently identify the needs and wants of their clientele. These organizations are reluctant to release information and statistics because they fear it would result in them losing their competitive edge and they would consequently lose the opportunity to benefit monetarily from the data collected. Data leaks would also result in the company getting a bad name and its reputation could be significantly hampered. Despite the individual anonymity, the transaction costs incurred in ensuring the data of their individual customers is protected is often an expensive process. In addition to this there is a definite human capital gap resulting from the significant lack of scientists and analysts to interpret raw data transmitted across various channels.</p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; "><b>(i) </b> <b>Big Data in Urban Planning </b></p>
<p style="text-align: justify; ">Urban planning would require data that is reflective of the land use patterns of communities, combined with their travel descriptions and housing preferences. The mobility of individuals is dependent on their economic conditions and can be determined through an analysis of their purchases, either via online transactions or from the data accumulated by prominent stores. The primary source of this data is however mobile phones, which seemed to have transcend economic barriers. Secondary sources include cards used on public transport such as the Oyster card in London and the similar Octopus card used in Hong Kong. However, in most developing countries these cards are not available for public transport systems and therefore mobile network data forms the backbone of data analytics. An excessive reliance on the data collected through Smart phones could however be detrimental, especially in developing countries, simply because the usage itself would most likely be concentrated amongst more economically stable demographics and the findings from this data could potentially marginalize the poor.<a href="#_ftn60" name="_ftnref60">[60]</a></p>
<p style="text-align: justify; ">Mobile network big data (MNBD) is generated by all phones and includes CDRs, which are obtained from calls or texts that are sent or received, internet usage, topping up a prepaid value and VLR or Visitor Location Registry data which is generated whenever the phone is question has power. It essentially communicates to the Base Transceiver Stations (BSTs) that the phone is in the coverage area. The CDR includes records of calls made, duration of the call and information about the device. It is therefore stored for a longer period of time. The VLR data is however larger in volume and can be written over. Both VLR and CDR data can provide invaluable information that can be used for urban planning strategies. <a href="#_ftn61" name="_ftnref61">[61]</a> LIRNE<i>asia, </i>a regional policy and regulation think-tank has carried out an extensive study demonstrating the value of MNBD in SriLanka.<a href="#_ftn62" name="_ftnref62">[62]</a> This has been used to understand and sometimes even monitor land use patterns, travel patterns during peak and off seasons and the congregation of communities across regions. This study was however only undertaken after the data had been suitably pseudonymised.<a href="#_ftn63" name="_ftnref63">[63]</a> The study revealed that MNBD was incredibly valuable in generating important information that could be used by policy formulators and decision makers, because of two primary characteristics. Firstly, it comes close to a comprehensive coverage of the demographic within developing countries, thus using mobile phones as sensors to generate useful data. Secondly, people using mobile phones across vast geographic areas reflect important information regarding patterns of their travel and movement. <a href="#_ftn64" name="_ftnref64">[64]</a></p>
<p style="text-align: justify; ">MNBD allows for the tracking and mapping of changes in population densities on a daily basis, thus identifying 'home' and 'work' locations, informing policy makers of population congestion so that thy may be able to formulate policies with respect to easing this congestion. According to Rohan Samarajiva, founding chair of LIRNEasia, "This allows for real-time insights on the geo-spatial distribution of population, which may be used by urban planners to create more efficient traffic management systems."<a href="#_ftn65" name="_ftnref65"><sup><sup>[65]</sup></sup></a> This can also be used for the developmental economic policies. For example, the northern region of Colombo, a region inhabited by the low income families shows a lower population density on weekdays. This is reflective of the large numbers travelling to southern Colombo for employment. <a href="#_ftn66" name="_ftnref66"><sup><sup>[66]</sup></sup></a>Similarly, patterns of land use can be ascertained by analyzing the various loading patterns of base stations. Building on the success of the Mobile Data analysis project in SriLanka LIRNEasia plans to collaborate with partners in India and Bangladesh to assimilate real time information about the behavioral tendencies of citizens, using which policy makers may be able to make informed decisions. When this data is combined with user friendly virtual platforms such as smartphone Apps or web portals, it can also help citizens make informed choices about their day to day activities and potentially beneficial long term decisions. <a href="#_ftn67" name="_ftnref67"><sup><sup>[67]</sup></sup></a></p>
<p style="text-align: justify; "><b><i>Challenges of using Mobile Network Data</i></b></p>
<p style="text-align: justify; "><b><i> </i></b></p>
<p style="text-align: justify; ">Mobile networks invest significant sums of money in obtaining information regarding usage patterns of their services. Consequently, they may use this data to develop location based advertizing. In this context, there is a greater reluctance to share data for public purposes. Allowing access to one operator's big data by another could result in significant implications on the other with respect to the competitive advantage shared by the operator. A plausible solution to this conundrum is the accumulation of data from multiple sources without separating or organizing it according to the source it originates from. There is thus a lesser chance of sensitive information of one company being used by another. However, even operators do have concerns about how the data would be handled before this "mashing up" occurs and whether it might be leaked by the research organization itself. LIRNE<i>asia </i>used comprehensive non-disclosure agreements to ensure that the researchers who worked with the data were aware of the substantial financial penalties that may be imposed on them for data breaches. The access to the data was also restricted. <a href="#_ftn68" name="_ftnref68">[68]</a></p>
<p style="text-align: justify; ">Another line of argumentation advocates for the open sharing of data. A recent article in the <i>Economist </i>has articulated this in the context of the Ebola outbreak in West Africa. " <i> Releasing the data, though, is not just a matter for firms since people's privacy is involved. It requires governmental action as well. Regulators in each affected country would have to order operators to make their records accessible to selected researchers, who through legal agreements would only be allowed to use the data in a specific manner. For example, Orange, a major mobile phone network operator has made millions of CDRs from Senegal and The Ivory Coast available for researchers for their use under its Data Development Initiative. However the Political will amongst regulators and Network operators to do this seems to be lacking."<a href="#_ftn69" name="_ftnref69"><b>[69]</b></a> </i></p>
<p style="text-align: justify; ">It would therefore be beneficial for companies to collaborate with the customers who create the data and the researchers who want to use it to extract important insights. This however would require the creation of and subsequent adherence to self regulatory codes of conduct. <a href="#_ftn70" name="_ftnref70">[70]</a> In addition to this cooperation between network operators will assist in facilitating the transference of the data of their customers to research organizations. Sri Lanka is an outstanding example of this model of cooperation which has enabled various operators across spectrums to participate in the mobile-money enterprise.<a href="#_ftn71" name="_ftnref71">[71]</a><b> </b></p>
<p style="text-align: justify; "><b>(ii) </b> <b>Big Data and Government Delivery of Services and Functions </b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">The analysis of Data procured in real time has proven to be integral to the formulation of policies, plans and executive decisions. Especially in an Asian context, Big data can be instrumental in urban development, planning and the allocation of resources in a manner that allows the government to keep up with the rapidly growing demands of an empowered population whose numbers are on an exponential rise. Researchers have been able to use data from mobile networks to engage in effective planning and management of infrastructure, services and resources. If, for example, a particular road or highway has been blocked for a particular period of time an alternative route is established before traffic can begin to build up creating a congestion, simply through an analysis of information collected from traffic lights, mobile networks and GPS systems.<a href="#_ftn72" name="_ftnref72">[72]</a></p>
<p style="text-align: justify; ">There is also an emerging trend of using big data for state controlled services such as the military. The South Korean Defense Minister Han Min Koo, in his recent briefing to President Park Geun-hye reflected on the importance of innovative technologies such as Big Data solutions. <a href="#_ftn73" name="_ftnref73">[73]</a></p>
<p style="text-align: justify; ">The Chinese government has expressed concerns regarding data breaches and information leakages that would be extremely dangerous given the exceeding reliance of governments on big data. A security report undertaken by Qihoo 360, China's largest software security provider established that 2,424 of the 17,875 Web security loopholes were on government websites. Considering the blurring line between government websites and external networks, it has become all the more essential for authorities to boost their cyber security protections.<a href="#_ftn74" name="_ftnref74">[74]</a></p>
<p style="text-align: justify; ">The Japanese government has considered investing resources in training more data scientists who may be able to analyze the raw data obtained from various sources and utilize requisite techniques to develop an accurate analysis. The Internal Affairs and Communication Ministry planned to launch a free online course on big data, the target of which would be corporate workers as well as government officials.<a href="#_ftn75" name="_ftnref75">[75]</a></p>
<p style="text-align: justify; ">Data analytics is emerging as an efficient technique of monitoring the public transport management systems within Singapore. A recent collaboration between IBM, StarHub, The Land Transport Authority and SMRT initiated a research study to observe the movement of commuters across regions. <a href="#_ftn76" name="_ftnref76"><sup><sup>[76]</sup></sup></a> This has been instrumental in revamping the data collection systems already in place and has allowed for the procurement of additional systems of monitoring.<a href="#_ftn77" name="_ftnref77"><sup><sup>[77]</sup></sup></a> The idea is essentially to institute a "black box" of information for every operational unit that allows for the relaying of real-time information from sources as varied as power switches, tunnel sensors and the wheels, through assessing patterns of noise and vibration. <a href="#_ftn78" name="_ftnref78"><sup><sup>[78]</sup></sup></a></p>
<p style="text-align: justify; ">In addition to this there are numerous projects in place that seek to utilize Big Data to improve city life. According to Carlo Ritti, Director of the MIT Senseable City Lab, "We are now able to analyze the pulse of a city from moment to moment. Over the past decade, digital technologies have begun to blanket our cities, forming the backbone of a large, intelligent infrastructure." <a href="#_ftn79" name="_ftnref79"><sup><sup>[79]</sup></sup></a> The professor of Information Architecture and Founding Director of the Singapore ETH Centre, Gerhart Schmitt has observed that "the local weather has a major impact on the behavior of a population." In this respect the centre is engaged in developing a range of visual platforms to inform citizens on factors such as air quality which would enable individuals to make everyday choices such as what route to take when planning a walk or predict a traffic jam. <a href="#_ftn80" name="_ftnref80"><sup><sup>[80]</sup></sup></a> Schmitt's team has also been able to arrive at a pattern that connects the demand for taxis with the city's climate. The amalgamation of taxi location with rainfall data has been able to help locals hail taxis during a storm. This form of data can be used in multiple ways allowing the visualization of temperature hotspots based on a "heat island" effect where buildings, cars and cooling units cause a rise in temperature. <a href="#_ftn81" name="_ftnref81"><sup><sup>[81]</sup></sup></a></p>
<p style="text-align: justify; ">Microsoft has recently entered into a partnership with the Federal University of Minas Gerais, one of the largest universities in Brazil to undertake a research project that could potentially predict traffic jams up to an hour in advance. <a href="#_ftn82" name="_ftnref82"><sup><sup>[82]</sup></sup></a> The project attempts to analyze information from transport departments, road traffic cameras and drivers social network profiles to identify patterns that they could use to help predict traffic jams approximately 15 to 60 minutes before they actually happen.<a href="#_ftn83" name="_ftnref83"><sup><sup>[83]</sup></sup></a></p>
<p style="text-align: justify; ">In anticipation of the increasing demand for professionals with requisite training in data sciences, the Malaysian Government has planned to increase the number of local data scientists from the present 80 to 1500 by 2020, through the support of the universities within the country.<b> </b></p>
<h3 style="text-align: justify; "><b>IV. </b> <b>Big Data and the Private Sector in the Global South </b></h3>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Essential considerations in the operations of Big Data in the Private sector in the Asia Pacific region have been extracted by a comprehensive survey carried out by the Economist Intelligence Unit.<a href="#_ftn84" name="_ftnref84">[84]</a> Over 500 executives across the Asia Pacific region were surveyed, from across industries representing a diverse range of functions. 69% of these companies had an annual turnover of over US $500m. The respondents were senior managers responsible for taking key decisions with regard to investment strategies and the utilization of big data for the same.</p>
<p style="text-align: justify; ">The results of the Survey conclusively determined that firms in the Asia Pacific region have had limited success with implementing Big Data Practices. A third of the respondents claimed to have an advanced knowledge of the utilization of big data while more than half claim to have made limited progress in this regard. Only 9% of the Firms surveyed cited internal barriers to implementing big data practices. This included a significant difficulty in enabling the sharing of information across boundaries. Approximately 40% of the respondents surveyed claimed they were unaware of big data strategies, even if they had in fact been in place simply because these had been poorly communicated to them. Almost half of the firms however believed that big data plays an important role in the success of the firm and that it can contribute to increasing revenue by 25% or more.</p>
<p style="text-align: justify; ">Numerous obstacles in the adoption of big data were cited by the respondents. These include the lack of suitable software to interpret the data and the lack of in-house skills to analyze the data appropriately. In addition to this, the lack of willingness on the part of various departments to share their data for the fear of a breach or leak was thought to be a major hindrance. This combined with a lack of communication between the various departments and exceedingly complicated reports that cannot be analyzed given the limited resources and lack of human capital qualified enough to carry out such an analysis, has resulted in an indefinite postponement of any policy propounding the adoption of big data practices.</p>
<p style="text-align: justify; ">Over 59% of the firms surveyed agreed that collaboration is integral to innovation and that information silos are a huge hindrance within a knowledge based economy. There is also a direct correlation between the size of the company and its progress in adopting big data, with larger firms adopting comprehensive strategies more frequently than smaller ones. A major reason for this is that large firms with substantially greater resources are able to actualize the benefits of big data analytics more efficiently than firms with smaller revenues. These businesses which have advanced policies in place outlining their strategies with respect to their reliance on big data are also more likely to communicate these strategies to their employees to ensure greater clarity in the process.</p>
<p style="text-align: justify; ">The use of big data was recently voted as the "best management practice" of the past year according to a cumulative ranking published by Chief Executive China Magazine, a Trade journal published by Global Sources on 13th January, 2015 in Beijing. The major benefit cited was the real-time information sourced from customers, which allows for direct feedback from clients when making decisions regarding changes in products or services. <a href="#_ftn85" name="_ftnref85">[85]</a></p>
<p style="text-align: justify; ">A significant contributor to the lack of adequate usage of data analytics is the belief that a PhD is a prerequisite for entering the field of data science. This misconception was pointed out by Richard Jones, vice president of Cloudera in the Australia, New Zealand and the Asean region. Cloudera provides businesses with the requisite professional services that they may need to effectively utilize Big Data. This includes a combination of the necessary manpower, technology and consultancy services.<a href="#_ftn86" name="_ftnref86"><sup><sup>[86]</sup></sup></a> Deepak Ramanathan, the chief technology officer, SAS Asia Pacific believes that this skill gap can be addressed by forming data science teams within both governments and private enterprises. These teams could comprise of members with statistical, coding and business skills and allow them to work in a collaborative manner to address the problem at hand.<a href="#_ftn87" name="_ftnref87"><sup><sup>[87]</sup></sup></a> SAS is an Enterprise Software Giant that creates tools tailored to suit business users to help them interpret big data. Eddie Toh, the planning and marketing manager of Intel's data center platform believes that businesses do not necessarily need data scientists to be able to use big data analytics to their benefit and can in fact outsource the technical aspects of the interpretation of this data as and when required.<a href="#_ftn88" name="_ftnref88"><sup><sup>[88]</sup></sup></a></p>
<p style="text-align: justify; ">The analytical team at Dell has forged a partnership with Brazilian Public Universities to facilitate the development of a local talent pool in the field of data analytics. The Instituto of Data Science (IDS) will provide training methodologies for in person or web based classes. <a href="#_ftn89" name="_ftnref89"><sup><sup>[89]</sup></sup></a> The project is being undertaken by StatSoft, a subsidiary of Dell that was acquired by the technology giant last year. <a href="#_ftn90" name="_ftnref90"><sup><sup>[90]</sup></sup></a></p>
<h3 style="text-align: justify; "><b>V. </b> <b>Conclusion</b></h3>
<p style="text-align: justify; "><b> </b></p>
<p style="text-align: justify; ">There have emerged numerous challenges in the analysis and interpretation of Big Data. While it presents an extremely engaging opportunity, which has the potential to transform the lives of millions of individuals, inform the private sector and influence government, the actualization of this potential requires the creation of a sustainable foundational framework ; one that is able to mitigate the various challenges that present themselves in this context.</p>
<p style="text-align: justify; ">A colossal increase in the rate of digitization has resulted in an unprecedented increment in the amount of Big Data available, especially through the rapid diffusion cellular technology. The importance of mobile phones as a significant source of data, especially in low income demographics cannot be overstated. This can be used to understand the needs and behaviors of large populations, providing an in depth insight into the relevant context within which valuable assessments as to the competencies, suitability and feasibilities of various policy mechanisms and legal instruments can be made. However, this explosion of data does have a lasting impact on how individuals and organizations interact with each other, which might not always be reflected in the interpretation of raw data without a contextual understanding of the demographic. It is therefore vital to employ the appropriate expertise in assessing and interpreting this data. The significant lack of a human resource to capital to analyze this information in an accurate manner poses a definite challenge to its effective utilization in the Global South.</p>
<p style="text-align: justify; ">The legal and technological implications of using Big Data are best conceptualized within the deliberations on protecting the privacy of the contributors to this data. The primary producers of this information, from across platforms, are often unaware that they are in fact consenting to the subsequent use of the data for purposes other than what was intended. For example people routinely accept terms and conditions of popular applications without understanding where or how the data that they inadvertently provide will be used.<a href="#_ftn91" name="_ftnref91">[91]</a> This is especially true of media generated on social networks that are increasingly being made available on more accessible platforms such as mobile phones and tablets. Privacy has and always will remain an integral pillar of democracy. It is therefore essential that policy makers and legislators respond effectively to possible compromises of privacy in the collection and interpretation of this data through the institution of adequate safeguards in this respect.</p>
<p style="text-align: justify; ">Another challenge that has emerged is the access and sharing of this data. Private corporations have been reluctant to share this data due to concerns about potential competitors being able to access and utilize the same. In addition to this, legal considerations also prevent the sharing of data collected from their customers or users of their services. The various technical challenges in storing and interpreting this data adequately also prove to be significant impediments in the collection of data. It is therefore important that adequate legal agreements be formulated in order to facilitate a reliable access to streams of data as well as access to data storage facilities to accommodate for retrospective analysis and interpretation.</p>
<p style="text-align: justify; ">In order for the use of Big Data to gain traction, it is important that these challenges are addressed in an efficient manner with durable and self-sustaining mechanisms of resolving significant obstructions. The debates and deliberations shaping the articulation of privacy concerns and access to such data must be supported with adequate tools and mechanisms to ensure a system of <i>"privacy-preserving analysis." The </i>UN Global Pulse has put forth the concept of data philanthropy to attempt to resolve these issues, wherein " <i>corporations </i>[would] <i> take the initiative to anonymize (strip out all personal information) their data sets and provide this data to social innovators to mine the data for insights, patterns and trends in realtime or near realtime."<a href="#_ftn92" name="_ftnref92"><b>[92]</b></a> </i></p>
<p style="text-align: justify; "><i> </i></p>
<p style="text-align: justify; ">The concept of data philanthropy highlights particular challenges and avenues that may be considered for future deliberations that may result in specific refinements to the process.</p>
<p style="text-align: justify; ">One of the primary uses of Big Data, especially in developing countries is to address important developmental issues such as the availability of clean water, food security, human health and the conservation of natural resources. Effective Disaster management has also emerged as one of the key functions of Big Data. It therefore becomes all the more important for organizations to assess the information supply chains pertaining to specific data sources in order to identify and prioritize the issues of data management. <a href="#_ftn93" name="_ftnref93">[93]</a> Data emerging from different contexts, across different sources may appear in varied compositions and would differ significantly across economic demographics. The Big Data generated from certain contexts would be inefficient due to the unavailability of data within certain regions and the resulting studies affecting policy decisions should take into account this discrepancy. This data unavailability has resulted in a digital divide which is especially prevalent in the global south. <a href="#_ftn94" name="_ftnref94">[94]</a></p>
<p style="text-align: justify; ">Appropriate analysis of the Big Data generated would provide a valuable insight into the key areas and inform policy makers with respect to important decisions. However, it is necessary to ensure that the quality of this data meets a specific standard and appropriate methodological processes have been undertaken to interpret and analyze this data. The government is a key actor that can shape the ecosystem surrounding the generation, analysis and interpretation of big data. It is therefore essential that governments of countries across the global south recognize the need to collaborate with civic organizations as well technical experts in order to create appropriate legal frameworks for the effective utilization of this data.</p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1">[1]</a> Onella, Jukka- Pekka. <i>"</i>Social Networks and Collective Human Behavior<i>." UN Global Pulse</i>. 10 Nov.2011. <http://www.unglobalpulse.org/node/14539></p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2">[2]</a> http://www.business2community.com/big-data/evaluating-big-data-predictive-analytics-01277835</p>
</div>
<div id="ftn3">
<p><a href="#_ftnref3" name="_ftn3">[3]</a> Ibid</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4">[4]</a> http://unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf</p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5">[5]</a> Ibid, p.13, pp.5</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6">[6]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7">[7]</a> Helbing, Dirk , and Stefano Balietti. "From Social Data Mining to Forecasting Socio-Economic Crises." <i>Arxiv </i>(2011) 1-66. 26 Jul 2011 http://arxiv.org/pdf/1012.0178v5.pdf.</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8">[8]</a> Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh andAngela H. Byers. <i>"</i>Big data: The next frontier for innovation, competition, and productivity.<i>" McKinsey</i></p>
<p><i>Global Institute </i> (2011): 1-137. May 2011.</p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9">[9]</a> "World Population Prospects, the 2010 Revision." <i>United Nations Development Programme.</i> <http://esa.un.org/unpd/wpp/unpp/panel_population.htm></p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10">[10]</a> Mobile phone penetration, measured by Google, from the number of mobile phones per 100 habitants, was 96% in Botswana, 63% in Ghana, 66% in Mauritania, 49% in Kenya, 47% in Nigeria, 44% in Angola, 40% in Tanzania (Source: Google Fusion Tables)</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11">[11]</a> http://www.brookings.edu/blogs/africa-in-focus/posts/2015/04/23-big-data-mobile-phone-highway-sy</p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12">[12]</a> Ibid</p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13">[13]</a> <http://www.google.com/fusiontables/Home/></p>
</div>
<div id="ftn14">
<p><a href="#_ftnref14" name="_ftn14">[14]</a> "Global Internet Usage by 2015 [Infographic]." <i>Alltop. </i><http://holykaw.alltop.com/global-internetusage-by-2015-infographic?tu3=1></p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15">[15]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011 <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16">[16]</a> Ibid</p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17">[17]</a> Ibid</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18">[18]</a> Ibid</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19">[19]</a> Goetz, Thomas. "Harnessing the Power of Feedback Loops." <i>Wired.com. </i>Conde Nast Digital, 19 June 2011. <http://www.wired.com/magazine/2011/06/ff_feedbackloop/all/1>.</p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20">[20]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21">[21]</a> Bollier, David. <i>The Promise and Peril of Big Data. </i>The Aspen Institute, 2010. <http://www.aspeninstitute.org/publications/promise-peril-big-data></p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22">[22]</a> Ibid</p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23">[23]</a> Eagle, Nathan and Alex (Sandy) Pentland. "Reality Mining: Sensing Complex Social Systems",<i>Personal and Ubiquitous Computing</i>, 10.4 (2006): 255-268.</p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24">[24]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25">[25]</a> OECD, Future Global Shocks, Improving Risk Governance, 2011</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26">[26]</a> "Economy: Global Shocks to Become More Frequent, Says OECD." <i>Organisation for Economic Cooperationand Development. </i>27 June. 2011.</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27">[27]</a> Friedman, Jed, and Norbert Schady. <i>How Many More Infants Are Likely to Die in Africa as a Result of the Global Financial Crisis? </i>Rep. The World Bank <http://siteresources.worldbank.org/INTAFRICA/Resources/AfricaIMR_FriedmanSchady_060209.pdf></p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28">[28]</a> Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute,June 2011<http://www.mckinsey.com/mgi/publications/big_data/pdfs/MGI_big_data_full_report.pdf></p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29">[29]</a> The word "crowdsourcing" refers to the use of non-official actors ("the crowd") as (free) sources of information, knowledge and services, in reference and opposition to the commercial practice of</p>
<p>outsourcing. "</p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30">[30]</a> Burke, J., D. Estrin, M. Hansen, A. Parker, N. Ramanthan, S. Reddy and M.B. Srivastava. <i>ParticipatorySensing. </i>Rep. Escholarship, University of California, 2006. <http://escholarship.org/uc/item/19h777qd>.</p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31">[31]</a> "Crisis Mappers Net-The international Network of Crisis Mappers." <http://crisismappers.net>, http://haiti.ushahidi.com and Goldman et al., 2009</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32">[32]</a> Alex Pentland cited in "When There's No Such Thing As Too Much Information". <i>The New York Times</i>.23 Apr. 2011<http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.</p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33">[33]</a> Nathan Eagle also cited in "When There's No Such Thing As Too Much Information". <i>The New YorkTimes</i>. 23 Apr. 2011. <http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34">[34]</a> Helbing and Balietti. "From Social Data Mining to Forecasting Socio-Economic Crisis."</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35">[35]</a> Eysenbach G. <i>Infodemiology: tracking flu-related searches on the Web for syndromic surveillance.</i>AMIA (2006)<http://yi.com/home/EysenbachGunther/publications/2006/eysenbach2006cinfodemiologyamia proc.pdf></p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36">[36]</a> Syndromic Surveillance (SS)." <i>Centers for Disease Control and Prevention. </i>06 Mar. 2012.<http://www.cdc.gov/ehrmeaningfuluse/Syndromic.html>.</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37">[37]</a> Health Map <http://healthmap.org/en/></p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38">[38]</a> see <a href="http://www.detective.io/">www.detective.io</a></p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39">[39]</a> www.ushahidi.com</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40">[40]</a> <a href="http://www.facebook.com/BlackMondayMovement">www.facebook.com/BlackMondayMovement</a></p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41">[41]</a> Ushahidi is a nonprofit tech company that was developed to map reports of violence in Kenya followingthe 2007 post-election fallout. Ushahidi specializes in developing "<i>free and open source software for</i></p>
<p><i>information collection, visualization and interactive mapping." </i> <http://ushahidi.com></p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42">[42]</a> Conducted by the European Commission's Joint Research Center against data on damaged buildingscollected by the World Bank and the UN from satellite images through spatial statistical techniques.</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43">[43]</a> www.ushahidi.com</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44">[44]</a> See https://<b>tacticaltech</b>.org/</p>
</div>
<div id="ftn45">
<p><a href="#_ftnref45" name="_ftn45">[45]</a> see www. flowminder.org</p>
</div>
<div id="ftn46">
<p><a href="#_ftnref46" name="_ftn46">[46]</a> Ibid</p>
</div>
<div id="ftn47">
<p><a href="#_ftnref47" name="_ftn47">[47]</a> <a href="http://post2015.unglobalpulse.net/">http://post2015.unglobalpulse.net/</a></p>
</div>
<div id="ftn48">
<p><a href="#_ftnref48" name="_ftn48">[48]</a> http://allafrica.com/stories/201507151726.html</p>
</div>
<div id="ftn49">
<p><a href="#_ftnref49" name="_ftn49">[49]</a> Ibid</p>
</div>
<div id="ftn50">
<p><a href="#_ftnref50" name="_ftn50">[50]</a> Ibid</p>
</div>
<div id="ftn51">
<p><a href="#_ftnref51" name="_ftn51">[51]</a> http://www.computerworld.com/article/2948226/big-data/opinion-apple-and-ibm-have-big-data-plans-for-education.html</p>
</div>
<div id="ftn52">
<p><a href="#_ftnref52" name="_ftn52">[52]</a> Ibid</p>
</div>
<div id="ftn53">
<p><a href="#_ftnref53" name="_ftn53">[53]</a> http://www.grameenfoundation.org/where-we-work/sub-saharan-africa/uganda</p>
</div>
<div id="ftn54">
<p><a href="#_ftnref54" name="_ftn54">[54]</a> Ibid</p>
</div>
<div id="ftn55">
<p><a href="#_ftnref55" name="_ftn55">[55]</a> http://chequeado.com/</p>
</div>
<div id="ftn56">
<p><a href="#_ftnref56" name="_ftn56">[56]</a> http://datochq.chequeado.com/</p>
</div>
<div id="ftn57">
<p><a href="#_ftnref57" name="_ftn57">[57]</a> <i>Times of India </i> (2015): "Chandigarh May Become India's First Smart City," 12 January, http://timesofi ndia.indiatimes.com/india/Chandigarh- may-become-Indias-fi rst-smart-city/articleshow/ 45857738.cms</p>
</div>
<div id="ftn58">
<p><a href="#_ftnref58" name="_ftn58">[58]</a> http://www.cisco.com/web/strategy/docs/scc/ioe_citizen_svcs_white_paper_idc_2013.pdf</p>
</div>
<div id="ftn59">
<p><a href="#_ftnref59" name="_ftn59">[59]</a> Townsend, Anthony M (2013): <i>Smart Cities: Big Data, Civic Hackers and the Quest for a New Utopia</i>, New York: WW Norton.</p>
</div>
<div id="ftn60">
<p><a href="#_ftnref60" name="_ftn60">[60]</a> See "Street Bump: Help Improve Your Streets" on Boston's mobile app to collect data on roadconditions, <a href="http://www.cityofboston.gov/DoIT/">http://www.cityofboston.gov/DoIT/</a> apps/streetbump.asp</p>
</div>
<div id="ftn61">
<p><a href="#_ftnref61" name="_ftn61">[61]</a> Mayer-Schonberger, V and K Cukier (2013): <i>Big Data: A Revolution That Will Transform How We Live, Work, and Think</i>, London: John Murray.</p>
</div>
<div id="ftn62">
<p><a href="#_ftnref62" name="_ftn62">[62]</a> http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html</p>
</div>
<div id="ftn63">
<p><a href="#_ftnref63" name="_ftn63">[63]</a> Ibid</p>
</div>
<div id="ftn64">
<p><a href="#_ftnref64" name="_ftn64">[64]</a> Newman, M E J and M Girvan (2004): "Finding and Evaluating Community Structure in Networks,"<i>Physical Review E, American Physical Society</i>, Vol 69, No 2.</p>
</div>
<div id="ftn65">
<p><a href="#_ftnref65" name="_ftn65">[65]</a> http://www.sundaytimes.lk/150412/sunday-times-2/big-data-can-make-south-asian-cities-smarter-144237.html</p>
</div>
<div id="ftn66">
<p><a href="#_ftnref66" name="_ftn66">[66]</a> Ibid</p>
</div>
<div id="ftn67">
<p><a href="#_ftnref67" name="_ftn67">[67]</a> Ibid</p>
</div>
<div id="ftn68">
<p><a href="#_ftnref68" name="_ftn68">[68]</a> http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html</p>
</div>
<div id="ftn69">
<p><a href="#_ftnref69" name="_ftn69">[69]</a> GSMA (2014): "GSMA Guidelines on Use of Mobile Data for Responding to Ebola," October, http:// <a href="http://www.gsma.com/mobilefordevelopment/wpcontent/">www.gsma.com/mobilefordevelopment/wpcontent/</a> uploads/2014/11/GSMA-Guidelineson-</p>
<p>protecting-privacy-in-the-use-of-mobilephone- data-for-responding-to-the-Ebola-outbreak-_ October-2014.pdf</p>
</div>
<div id="ftn70">
<p><a href="#_ftnref70" name="_ftn70">[70]</a> An example of the early-stage development of a self-regulatory code may be found at http:// lirneasia.net/2014/08/what-does-big-data-sayabout- sri-lanka/</p>
</div>
<div id="ftn71">
<p><a href="#_ftnref71" name="_ftn71">[71]</a> See "Sri Lanka's Mobile Money Collaboration Recognized at MWC 2015," <a href="http://lirneasia/">http://lirneasia</a>. net/2015/03/sri-lankas-mobile-money-colloboration- recognized-at-mwc-2015/</p>
</div>
<div id="ftn72">
<p><a href="#_ftnref72" name="_ftn72">[72]</a> http://www.thedailystar.net/big-data-for-urban-planning-57593</p>
</div>
<div id="ftn73">
<p><a href="#_ftnref73" name="_ftn73">[73]</a> <a href="http://koreaherald.com/">http://koreaherald.com</a> , 19/01/2015</p>
</div>
<div id="ftn74">
<p><a href="#_ftnref74" name="_ftn74">[74]</a> http://www.news.cn/, 25/11/2014</p>
</div>
<div id="ftn75">
<p><a href="#_ftnref75" name="_ftn75">[75]</a> <a href="http://the-japan-news.com/">http://the-japan-news.com</a> , 20/01/2015</p>
</div>
<div id="ftn76">
<p><a href="#_ftnref76" name="_ftn76">[76]</a> http://www.todayonline.com/singapore/can-big-data-help-tackle-mrt-woes</p>
</div>
<div id="ftn77">
<p><a href="#_ftnref77" name="_ftn77">[77]</a> Ibid</p>
</div>
<div id="ftn78">
<p><a href="#_ftnref78" name="_ftn78">[78]</a> Ibid</p>
</div>
<div id="ftn79">
<p><a href="#_ftnref79" name="_ftn79">[79]</a> http://edition.cnn.com/2015/06/24/tech/big-data-urban-life-singapore/</p>
</div>
<div id="ftn80">
<p><a href="#_ftnref80" name="_ftn80">[80]</a> Ibid</p>
</div>
<div id="ftn81">
<p><a href="#_ftnref81" name="_ftn81">[81]</a> Ibid</p>
</div>
<div id="ftn82">
<p><a href="#_ftnref82" name="_ftn82">[82]</a> http://venturebeat.com/2015/04/03/how-microsofts-using-big-data-to-predict-traffic-jams-up-to-an-hour-in-advance/</p>
</div>
<div id="ftn83">
<p><a href="#_ftnref83" name="_ftn83">[83]</a> Ibid</p>
</div>
<div id="ftn84">
<p><a href="#_ftnref84" name="_ftn84">[84]</a> https://www.hds.com/assets/pdf/the-hype-and-the-hope-summary.pdf</p>
</div>
<div id="ftn85">
<p><a href="#_ftnref85" name="_ftn85">[85]</a> <a href="http://www.news.cn/">http://www.news.cn</a> , 14/01/2015</p>
</div>
<div id="ftn86">
<p><a href="#_ftnref86" name="_ftn86">[86]</a> http://www.techgoondu.com/2015/06/29/plugging-the-big-data-skills-gap/</p>
</div>
<div id="ftn87">
<p><a href="#_ftnref87" name="_ftn87">[87]</a> Ibid</p>
</div>
<div id="ftn88">
<p><a href="#_ftnref88" name="_ftn88">[88]</a> Ibid</p>
</div>
<div id="ftn89">
<p><a href="#_ftnref89" name="_ftn89">[89]</a> http://www.zdnet.com/article/dell-to-create-big-data-skills-in-brazil/</p>
</div>
<div id="ftn90">
<p><a href="#_ftnref90" name="_ftn90">[90]</a> Ibid</p>
</div>
<div id="ftn91">
<p><a href="#_ftnref91" name="_ftn91">[91]</a> Efrati, Amir. "'Like' Button Follows Web Users." <i>The Wall Street Journal. </i>18 May 2011.</p>
<p><http://online.wsj.com/article/SB10001424052748704281504576329441432995616.html></p>
</div>
<div id="ftn92">
<p><a href="#_ftnref92" name="_ftn92">[92]</a> Krikpatrick, Robert. "Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience."</p>
<p><i>UN Global Pulse. </i> 16 Sept. 2011. <http://www.unglobalpulse.org/blog/data-philanthropy-public-privatesector-data-sharing-global-resilience></p>
</div>
<div id="ftn93">
<p><a href="#_ftnref93" name="_ftn93">[93]</a> Laney D (2001) 3D data management: Controlling data volume, velocity and variety. Available at: http://blogs. gartner.com/doug-laney/files/2012/01/ad949-3D-DataManagement-Controlling-Data-Volume-Velocity-andVariety.pdf</p>
</div>
<div id="ftn94">
<p><a href="#_ftnref94" name="_ftn94">[94]</a> Boyd D and Crawford K (2012) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society 15(5): 662-679.</p>
</div>
</div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis'>http://editors.cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis</a>
</p>
No publishertanviInternet GovernanceBig Data2016-01-24T02:54:45ZBlog EntryThe Changing Landscape of ICT Governance and Practice - Convergence and Big Data
http://editors.cis-india.org/internet-governance/news/the-changing-landscape-of-ict-governance-and-practice-convergence-and-big-data
<b></b>
<p style="text-align: justify; ">Sharat Chandra Ram was granted the <a href="http://www.cprsouth.org/2015/02/call-for-applications-2015-young-scholar-awards/">Young Scholar Award 2015</a> to attend the <i>Young Scholar Workshop (August 24 - 25, 2015)</i> followed by main <a href="http://www.cprsouth.org/"><i>CPRSouth2015 conference</i> (Communication Policy Research South) conference <i>(26th - 28th August 2015</i>)</a> - "The Changing Landscape of ICT Governance and Practice - Convergence and Big Data" that was co-organized by the 'Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan. The agenda for Young Scholar 2015 pre-conferernce workshop can be accessed <a class="external-link" href="http://www.cprsouth.org/cprsouth-2015-call-for-abstracts/cprsouth-2015-young-scholar-awards-call-for-applications/">here</a>. The CPR South 2015: Conference Programme agenda can be accessed <a class="external-link" href="http://www.cprsouth.org/cprsouth-2015-call-for-abstracts/cpr-south-2015-conference-programme/">here</a>.</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/the-changing-landscape-of-ict-governance-and-practice-convergence-and-big-data'>http://editors.cis-india.org/internet-governance/news/the-changing-landscape-of-ict-governance-and-practice-convergence-and-big-data</a>
</p>
No publisherpraskrishnaInternet GovernanceBig Data2015-09-07T13:48:37ZNews ItemBig Data and Reproductive Health in India: A Case Study of the Mother and Child Tracking System
http://editors.cis-india.org/raw/big-data-reproductive-health-india-mcts
<b>In this case study undertaken as part of the Big Data for Development (BD4D) network, Ambika Tandon evaluates the Mother and Child Tracking System (MCTS) as data-driven initiative in reproductive health at the national level in India. The study also assesses the potential of MCTS to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data.</b>
<p> </p>
<h4>Case study: <a href="https://github.com/cis-india/website/raw/master/bd4d/CIS_CaseStudy_AT_BigDataReproductiveHealthMCTS.pdf" target="_blank">Download</a> (PDF)</h4>
<hr />
<h3>Introduction</h3>
<p>The reproductive health information ecosystem in India comprises of a range of different databases across state and national levels. These collect data through a combination of manual and digital tools. Two national-level databases have been launched by the Ministry of Health and Family Welfare - the Health Management Information System (HMIS) in 2008, and the MCTS in 2009. 4 The MCTS focuses on collecting data on maternal and child health. It was instituted due to reported gaps in the HMIS, which records monthly data across health programmes including reproductive health. There are several other state-level initiatives on reproductive health data that have either been subsumed into, or run in
parallel with, the MCTS.</p>
<p>With this case study, we aim to evaluate the MCTS as data-driven initiative in reproductive health at the national level. It will also assess its potential to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data. The methodology for the case study involved a desk-based review of existing literature on the use of health information systems globally, as well as analysis of government reports, journal articles, media coverage, policy documents, and other material on the MCTS.</p>
<p>The first section of this report details the theoretical framing of the case study, drawing on the feminist critique of reproductive data systems. The second section maps the current landscape of reproductive health data produced by the state in India, with a focus on data flows, and barriers to data collection and analysis at the local and national level. The case of abortion data is used to further the argument of flawed data collection systems at the
national level. Section three briefly discusses the state’s imagination of reproductive health policy and the role of data systems through a discussion on the National Health Policy, 2017 and the National Health Stack, 2018. Finally, we make some policy recommendations and identify directions for future research, taking into account the ongoing shift towards big data globally to democratise reproductive healthcare.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/raw/big-data-reproductive-health-india-mcts'>http://editors.cis-india.org/raw/big-data-reproductive-health-india-mcts</a>
</p>
No publisherambikaBig DataData SystemsResearchers at WorkReproductive and Child HealthResearchFeaturedPublicationsBD4DHealthcareBig Data for Development2019-12-06T04:57:55ZBlog Entry