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Report 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 EntryPrivacy in the Age of Big Data
http://editors.cis-india.org/internet-governance/blog/asian-age-amber-sinha-april-10-2017-privacy-in-the-age-of-big-data
<b>Personal data is freely accessible, shared and even sold, and those to whom this information belongs have little control over its flow.</b>
<p style="text-align: justify; ">The article was published in the <a class="external-link" href="http://www.asianage.com/india/all-india/100417/privacy-in-the-age-of-big-data.html">Asian Age</a> on April 10, 2017.</p>
<hr style="text-align: justify; " />
<p style="text-align: justify; ">In 2011 it was estimated that the quantity of data produced globally surpassed 1.8 zettabyte. By 2013, it had increased to 4 zettabytes. This is a result of digital services which involve constant data trails left behind by human activity. This expansion in the volume, velocity, and variety of data available, together with the development of innovative forms of statistical analytics on the data collected, is generally referred to as “Big Data”. Despite significant (though largely unrealised) promises about Big Data, which range from improved decision-making, increased efficiency and productivity to greater personalisation of services, concerns remain about the impact of such datafication of all human activity on an individual’s privacy. Privacy has evolved into a sweeping concept, including within its scope matters pertaining to control over one’s body, physical space in one’s home, protection from surveillance, and from search and seizure, protection of one’s reputation as well as one’s thoughts. This generalised and vague conception of privacy not only comes with great judicial discretion, it also thwarts a fair understanding of the subject. Robert Post called privacy a concept so complex and “entangled in competing and contradictory dimensions, so engorged with various and distinct meanings”, that he sometimes “despairs whether it can be usefully addressed at all”.</p>
<p style="text-align: justify; ">This also leaves the idea of privacy vulnerable to considerable suspicion and ridicule. However, while there is a lack of clarity over the exact contours of what constitutes privacy, there is general agreement over its fundamental importance to our ability to lead whole lives. In order to understand the impact of datafied societies on privacy, it is important to first delve into the manner in which we exercise our privacy. The ideas of privacy and data management that are prevalent can be traced to the Fair Information Practice Principles (FIPP). These principles are the forerunners of most privacy regimes internationally, such as the OECD Privacy Guidelines, APEC Framework, or the nine National Privacy Principles articulated by the Justice A.P. Shah Committee Report. All of these frameworks have rights to notice, consent and correction, and how the data may be used, as their fundamental principles. It makes the data subject to the decision-making agent about where and when her/his personal data may be used, by whom, and in what way. The individual needs to be notified and his consent obtained before his personal data is used. If the scope of usage extends beyond what he has agreed to, his consent will be required for the increased scope.</p>
<p style="text-align: justify; ">In theory, this system sounds fair. Privacy is a value tied to the personal liberty and dignity of an individual. It is only appropriate that the individual should be the one holding the reins and taking the large decisions about the use of his personal data. This makes the individual empowered and allows him to weigh his own interests in exercising his consent. The allure of this paradigm is that in one elegant stroke, it seeks to ensure that consent is informed and free and also to implement an acceptable trade-off between privacy and competing concerns. This approach worked well when the number of data collectors were less and the uses of data was narrower and more defined. Today’s infinitely complex and labyrinthine data ecosystem is beyond the comprehension of most ordinary users. Despite a growing willingness to share information online, most people have no understanding of what happens to their data.</p>
<p style="text-align: justify; ">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, “creating countless new digital puddles, lakes, tributaries and oceans of information”. The inadequacy of the regulatory approaches and the absence of a comprehensive data protection regulation is exacerbated by the emergence of data-driven business models in the private sector and the adoption of data-driven governance approach by the government. The Aadhaar project, with over a billion registrants, is intended to act as a platform for a number of digital services, all of which produce enormous troves of data. The original press release by the Central Government reporting the approval by the Cabinet of Ministers of the Digital India programme, speaks of “cradle to grave” digital identity as one of its vision areas.</p>
<p style="text-align: justify; ">While the very idea of the government wanting to track its citizens’ lives from cradle to grave is creepy enough in itself, let us examine for a minute what this form of datafied surveillance will entail. A host of schemes under Digital India shall collect and store information through the life cycle of an individual. The result, as we can see, is building databases on individuals, which when combined, will provide a 360 degree view into the lives of individuals. Alongside the emergence of India Stack, a set of APIs built on top of the Aadhaar, conceptualised by iSPIRT, a consortium of select IT companies from India, to be deployed and managed by several agencies, including the National Payments Corporation of India, promises to provide a platform over which different private players can build their applications.</p>
<p style="text-align: justify; ">The sum of these interconnected parts will lead to a complete loss of anonymity, greater surveillance and impact free speech and individual choice. The move towards a cashless economy — with sharp nudges from the government — could lead to lack of financial agencies in case of technological failures as has been the case in experiments with digital payments in Africa. Lack of regulation in emerging data driven sectors such as Fintech can enable predatory practices where right to remotely deny financial services can be granted to private sector companies. An architecture such as IndiaStack enables datafication of financial transactions in a way that enables linked and structured data that allows continued use of the transaction data collected. It is important to recognise that at the stage of giving consent, there are too many unknowns for us to make informed decisions about the future uses of our personal data. Despite blanket approvals allowing any kind of use granted contractually through terms of use and privacy policies, there should be legal obligations overriding this consent for certain kinds of uses that may require renewed consent.</p>
<p style="text-align: justify; "><b>Biometrics-based identification in UK: </b>In 2005, researchers from London School of Economics and Political Science came out with a detailed report on the UK Identity Cards Bill (‘UK Bill’) — the proposed legislation for a national identification system based on biometrics. The project also envisaged a centralised database (like India) that would store personal information along with the entire transaction history of every individual. The report pointed strongly against the centralising storage of information and suggested other alternatives such as a system based on smartcards (where biometrics are stored on the card itself) or offline biometric-reader terminals.</p>
<p style="text-align: justify; ">As per the report, the alternatives would also have been cheaper as neither required real-time online connectivity. In India, online authentication is a far greater challenge. According to Network Readiness Index, 2016, India ranks 91, whereas UK is placed eight. Poor Internet connectivity can raise a lot of problems in the future including paralysis of transactions. The UK identification project was subsequently discarded as a result of the privacy and cost considerations raised in this report.</p>
<h3 style="text-align: justify; ">Aadhaar: Privacy concerns</h3>
<ol style="text-align: justify; ">
<li>Once the data is collected through National Information Utilities, it will be privatised and controlled by private utilities.</li>
<li>Once an individual’s data is entered in the system, it cannot be deleted. That individual will have no control over it.</li>
<li>Aadhaar Data (Demographic details along with photographs) are shared/transferred with the private entities including telecom companies as per the Aadhaar (Targeted delivery of Financial and other subsidies, benefits and services) Act, 2016 with the consent of Aadhaar number holder to fulfil their e-KYC requirements. The data is shared in encrypted form through secured channel.</li>
<li>Aadhaar Enabled Payment System (AEPS) on which 119 banks are live.</li>
<li>More than 33.87 crore transactions have taken place through AEPS, which was only 46 lakhs in May 2014.</li>
<li>As on 30-9-2016, 78 government schemes were linked to Aadhaar.</li>
<li>The Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act, 2016, provides that no core-biometric information (fingerprints, iris scan) shall be shared with anyone for any reason whatsoever (Sec 29) and that the biometric information shall not be used for any purpose other than generation of Aadhaar and authentication.</li>
<li>Access to the data repository of UIDAI, called the Central Identities Data Repository(CIDR), is provided to third parties or private companies.</li>
</ol>
<p style="text-align: justify; "><b>Central Monitoring System</b> (CMS) is already live in Delhi, New Delhi and Mumbai. Union minister Ravi Shankar Prasad revealed this in one of his replies in the Lok Sabha last year. CMS has been set up to automate the process of Lawful Interception & Monitoring of telecommunications.</p>
<p style="text-align: justify; "><b>Central Monitoring System</b> (CMS) is already live in Delhi, New Delhi and Mumbai. Union minister Ravi Shankar Prasad revealed this in one of his replies in the Lok Sabha last year. CMS has been set up to automate the process of Lawful Interception & Monitoring of telecommunications.</p>
<p style="text-align: justify; "><b>Lawful Intercept </b>and Monitoring (LIM) systems are used by the Indian Government to intercept records of voice, SMSes, GPRS data, details of a subscriber’s application and recharge history and call detail record (CDR) and monitor Internet traffic, emails, web-browsing, Skype and any other Internet activity of Indian users.</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/asian-age-amber-sinha-april-10-2017-privacy-in-the-age-of-big-data'>http://editors.cis-india.org/internet-governance/blog/asian-age-amber-sinha-april-10-2017-privacy-in-the-age-of-big-data</a>
</p>
No publisheramberInternet GovernanceAadhaarBig DataPrivacy2017-04-11T14:43:59ZBlog EntryPrivacy after Big Data: Compilation of Early Research
http://editors.cis-india.org/internet-governance/blog/privacy-after-big-data-compilation-of-early-research
<b>Evolving data science, technologies, techniques, and practices, including big data, are enabling shifts in how the public and private sectors carry out their functions and responsibilities, deliver services, and facilitate innovative production and service models to emerge. In this compilation we have put together a series of articles that we have developed as we explore the impacts – positive and negative – of big data. This is a growing body of research that we are exploring and
is relevant to multiple areas of our work including privacy and surveillance. Feedback and comments on the compilation are welcome and appreciated.</b>
<p> </p>
<h4><a href="https://github.com/cis-india/website/raw/master/docs/CIS_PrivacyAfterBigData_CompilationOfEarlyResearch_2016.11.pdf">Download the Compilation</a> (PDF)</h4>
<hr />
<h3><strong>Privacy after Big Data</strong></h3>
<p>Evolving data science, technologies, techniques, and practices, including big data, are enabling shifts in how the public and private sectors carry out their functions and responsibilities, deliver services, and facilitate innovative production and service models to emerge. For example, in the public sector, the Indian government has considered replacing the traditional poverty line with targeted subsidies based on individual household income and assets. The my.gov.in platform is aimed to enable participation of the connected citizens, to pull in online public opinion in a structured manner on key governance topics in the country. The 100 Smart Cities Mission looks forwards to leverage big data analytics and techniques to deliver services and govern citizens within city sub-systems. In the private sector, emerging financial technology companies are developing credit scoring models using big, small, social, and fragmented data so that people with no formal credit history can be offered loans. These models promote efficiency and reduction in cost through personalization and are powered by a wide variety of data sources including mobile data, social media data, web usage data, and passively collected data from usages of IoT or connected devices.</p>
<p>These data technologies and solutions are enabling business models that are based on the ideals of ‘less’: cash-less, presence-less, and paper-less. This push towards an economy premised upon a foundational digital ID in a prevailing condition of absent legal frameworks leads to substantive loss of anonymity and privacy of individual citizens and consumers vis-a-vis both the state and the private sector. Indeed, the present use of these techniques run contrary to the notion of the ‘sunlight effect’ - making the individual fully transparent (often without their knowledge) to the state and private sector, while the algorithms and means of reaching a decision are opaque and inaccessible to the individual.</p>
<p>These techniques, characterized by the volume of data processed, the variety of sources data is processed from, and the ability to both contextualize - learning new insights from disconnected data points - and de-contextualize - finding correlation rather than causation - have also increased the value of all forms of data. In some ways, big data has made data exist on an equal playing field as far as monetisation and joining up are concerned. Meta data can be just as valuable to an entity as content data. As data science techniques evolve to find new ways of collecting, processing, and analyzing data - the benefits of the same are clear and tangible, while the harms are less clear, but significantly present.</p>
<p>Is it possible for an algorithm to discriminate? Will incorrect decisions be made based on data collected? Will populations be excluded from necessary services if they do not engage with certain models or do emerging models overlook certain populations? Can such tools be used to surveil individuals at a level of granularity that was formerly not possible and before a crime occurs? Can such tools be used to violate rights – for example target certain types of speech or groups online? And importantly, when these practices are opaque to the individual, how can one seek appropriate and effective remedy.</p>
<p>Traditionally, data protection standards have defined and established protections for certain categories of data. Yet, data science techniques have evolved beyond data protection principles. It is now infinitely harder to obtain informed consent from an individual when data that is collected can be used for multiple purposes by multiple bodies. Providing notice for every use is also more difficult – as is fulfilling requirements of data minimization. Some say privacy is dead in the era of big data. Others say privacy needs to be re-conceptualized, while others say protecting privacy now, more than ever, requires a ‘regulatory sandbox’ that brings together technical design, markets, legislative reforms, self regulation, and innovative regulatory frameworks. It also demands an expanding of the narrative around privacy – one that has largely been focused on harms such as misuse of data or unauthorized collection – to include discrimination, marginalization, and competition harms.</p>
<p>In this compilation we have put together a series of articles that we have developed as we explore the impacts – positive and negative – of big data. This includes looking at India’s data protection regime in the context of big data, reviewing literature on the benefits of harms of big data, studying emerging predictive policing techniques that rely on big data, and analyzing closely the impact of big data on specific privacy principles such as consent. This is a growing body of research that we are exploring and is relevant to multiple areas of our work including privacy and surveillance. Feedback and comments on the compilation are welcome and appreciated.</p>
<p><em>Elonnai Hickok</em><br />Director - Internet Governance</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/privacy-after-big-data-compilation-of-early-research'>http://editors.cis-india.org/internet-governance/blog/privacy-after-big-data-compilation-of-early-research</a>
</p>
No publisherSaumyaa NaiduHuman RightsIT ActBig DataPrivacyInternet GovernanceSmart CitiesData ProtectionInformation TechnologyPublications2016-11-12T01:37:03ZBlog EntryPress Release, March 15, 2016: The New Bill Makes Aadhaar Compulsory!
http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-15032016-the-new-bill-makes-aadhaar-compulsory
<b>We published and circulated the following press release on March 15, 2016, to highlight the fact that the Section 7 of the Aadhaar Bill, 2016 states that authentication of the person using her/his Aadhaar number can be made mandatory for the
purpose of disbursement of government subsidies, benefits, and services; and in case the person does not have an Aadhaar number, s/he will have to apply for Aadhaar enrolment. </b>
<p> </p>
<p>Nandan Nilekani, the former chairperson of the Unique Identification Authority of India had repeatedly stated that Aadhaar is not mandatory. However, in the last few years various agencies and departments of the government, both at the central and state level, had made it mandatory in order to be able to avail beneficiary schemes or for the arrangement of salary, provident fund disbursals, promotion, scholarship, opening bank account, marriages and property registrations. In August 2015, the Supreme Court passed an order mandating that the Aadhaar number shall
remain optional for welfare schemes, stating that no person should be denied any benefit for reason of not having an Aadhaar number, barring a few specified services.</p>
<p>The Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act, 2016, however, has not followed this mandate. Section 7 of the Bill states that “a person should be authenticated or give proof of the Aadhaar number to establish his/her identity” “as a condition for receiving subsidy, benefit or service”. Further, it reads, “In the case a person does not have an Aadhaar number, he/she should make an application for enrollment.” The language of the provision is very clear in making enrollment in Aadhaar mandatory, in order to be entitled for welfare services. Section 7 also says that “the person will be offered viable and alternate means of identification for receiving the subsidy, benefit or service. However, these unspecified alternate means will be made available in the event “an Aadhaar number is not assigned”. This language is vague and it is not clear whether it mandates alternate means of identification for those who choose not to apply for an Aadhaar number for any reason. The fact that it does make it mandatory to apply for an Aadhaar number for persons without it, may lead to the presumption that the alternate means are to be made available for those who may have applied for an Aadhaar number but it has not been assigned for any reason. It is also noteworthy that draft legislation is silent on what the “viable and
alternate means of identification” could be. There are a number of means of identification, which are recognised by the state, and a schedule with an inclusive list could have gone a long way in reducing the ambiguity in this provision.</p>
<p>Another aspect of Section 7 which is at odds with the Supreme Court order is that it allows making an Aadhaar number mandatory for “for receipt of a subsidy, benefit or service for which the expenditure is incurred” from the Consolidated Fund of India. The Supreme Court had been very specific in articulating that having an Aadhaar number could not be made compulsory except for “any purpose other than the PDS Scheme and in particular for the purpose of distribution of foodgrains, etc. and cooking fuel, such as kerosene” or for the purpose of the LPG scheme. The restriction in the Supreme Court order was with respect to the welfare schemes, however, instead of specifying the schemes, Section 7 specified the source of expenditure from which subsidies, benefits and services can be funded, making the scope much broader. Section 7, in effect, allows the Central Government to circumvent the Supreme Court
order if they choose to tie more subsidies, benefits and services to the Consolidated Fund of India.</p>
<p>These provisions run counter to the repeated claims of the government for the last six years that Aadhaar is not compulsory, nor is the specification by the Supreme Court for restricting use of Aadhaar to a few services only, reflected anywhere in the Bill. The “viable and alternate means” clause is too vague and inadequate to prevent denial of benefits to those without an Aadhaar number. The sum effect of these factors is to give the Central Government powers to make Aadhaar mandatory, for all practical purposes.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-15032016-the-new-bill-makes-aadhaar-compulsory'>http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-15032016-the-new-bill-makes-aadhaar-compulsory</a>
</p>
No publisherAmber SinhaUIDBig DataPrivacyInternet GovernanceDigital IndiaAadhaarBiometrics2016-03-16T10:11:32ZBlog EntryPress Release, March 11, 2016: The Law cannot Fix what Technology has Broken!
http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-11032016-the-law-cannot-fix-what-technology-has-broken
<b>We published and circulated the following press release on March 11, 2016, as the Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. This Bill was proposed by finance minister, Mr. Arun Jaitley to give legislative backing to Aadhaar, being implemented by the Unique Identification Authority of India (UIDAI).</b>
<p> </p>
<p>The Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016 today. This Bill was proposed by finance minister, Mr. Arun Jaitley to give legislative backing to Aadhaar, being implemented by the Unique Identification Authority of India (UIDAI).</p>
<p>The Bill was introduced as a money bill and there was no public consultation to evaluate the provisions therein even though there are very serious ramifications for the Right to Privacy and the Right to Association and Assembly. The Bill has made it compulsory for an individual to enrol under Aadhaar in order to receive any subsidy,
benefit or service from the Government. Biometric information that is required for the purpose of enrolment has been deemed "sensitive personal information" and restrictions have been imposed on use, disclosure and sharing of such information for purposes other than authentication, disclosure made pursuant to a court order or in the interest of national security. Here, the Bill has acknowledged the standards of protection of sensitive personal information established under Section 43A of the Information Technology Act, 2000. The Bill has also laid down several penal provisions for acts that include impersonation at the time of enrolment, unauthorised access to the
Central Identities Data Repository, unauthorised use by requesting entity, noncompliance with intimation requirements, etc.</p>
<h3>Key Issues</h3>
<h4>1. Identification without Consent</h4>
<p>Before the Aadhaar project it was not possible for the Indian government to identify citizens without their consent. But once the government has created a national centralized biometric database it will be possible for the government to identify any citizen without their consent. Hi-resolution photography and videography make it trivial for governments and also any other actor to harvest biometrics remotely. In other words, the technology makes consent irrelevant. A German ministers fingerprints were captured by hackers as she spoke using hand gesture at at conference. In a similar manner the government can now identify us both as individuals and also as groups without requiring our cooperation. This has direct implications for the right to privacy as we will be under constant government surveillance in the future as CCTV camera resolutions improve and there will be chilling effects on the
right to free speech and the freedom of association. The only way to fix this is to change the technology configuration and architecture of the project. The law cannot be used as band-aid on really badly designed technology.</p>
<h4>2. Fallible Technology</h4>
<p>The technology used for collection and authentication as been said to be fallible. It is understood that the technology has been feasible for a population of 200 million. The Biometrics Standards Committee of UIDAI has acknowledged the lack of data on how a biometric authentication technology will scale up where the population is about 1.2 billion. Further, a report by 4G Identity Solutions estimates that while in any population, approximately 5% of the people have unreadable fingerprints, in India it could lead to a failure to enroll up to 15% of the population.</p>
<p>We know that the Aadhaar number has been issued to dogs, trees (with the Aadhaar letter containing the photo of a tree). There have been slip-ups in the Aadhaar card enrolment process, some cards have ended up with
pictures of an empty chair, a tree or a dog instead of the actual applicants. An RTI application has revealed that the Unique Identification Authority of India (UIDAI) has identified more than 25,000 duplicate Aadhaar numbers in the country till August 2015.</p>
<p>At the stage of authentication, the accuracy of biometric identification depends on the chance of a false positive— the probability that the identifiers of two persons will match. For the current population of 1.2 billion the expected proportion of duplicates is 1/121, a ratio which is far too high. In a recent paper in EPW by Hans Mathews, a mathematician with CIS, shows that as per UIDAI's own statistics on failure rates, the programme would badly fail to uniquely identify individuals in India. <strong>[1]</strong></p>
<h3>Endnote</h3>
<p><strong>[1]</strong> See: <a href="http://cis-india.org/internet-governance/blog/epw-27-february-2016-hans-varghese-mathews-flaws-in-uidai-process">http://cis-india.org/internet-governance/blog/epw-27-february-2016-hans-varghese-mathews-flaws-in-uidai-process</a></p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-11032016-the-law-cannot-fix-what-technology-has-broken'>http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-11032016-the-law-cannot-fix-what-technology-has-broken</a>
</p>
No publisherJapreet Grewal and Sunil AbrahamUIDBig DataPrivacyInternet GovernanceDigital IndiaAadhaarBiometrics2016-03-16T10:10:40ZBlog 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 EntryOPINION | Data is New Oil and Human Mind the New Battlefield. India Must Wake Up Now
http://editors.cis-india.org/internet-governance/news/news-18-lt-general-retd-ds-hooda-data-is-new-oil-and-human-mind-the-new-battlefield-india-must-wake-up-now
<b>In information warfare, the battlespace is the human mind. This is where the privacy of an individual intersects with national security. Fighting this battle will require a new paradigm in thought and action.</b>
<p style="text-align: justify; ">The article by Lt. General (Retd.) D. S. Hooda was published by <a class="external-link" href="http://www.news18.com/news/india/opinion-data-is-new-oil-and-human-mind-the-new-battlefield-india-must-wake-up-now-1573747.html">News18.com</a> on November 11, 2017</p>
<hr style="text-align: justify; " />
<p style="text-align: justify; ">A few days ago, the Army Headquarters took out a public advisory warning about a “deliberate misinformation campaign being launched by vested interests some of which is being initiated from countries bordering our nation.” This is an acknowledgment of the use of social media for what is today considered the most dominant form of warfare — ‘information warfare’. It has been extensively used by our adversaries in Jammu and Kashmir to show the government and security forces in poor light.<br /> <br /> Deception, propaganda and misinformation have always been a part of warfare but what is different today is that the tools of information warfare have acquired a new dimension. An integration of massive amounts of data with Artificial Intelligence (AI) has given a significant weapon in the hands of information warriors.</p>
<p style="text-align: justify; ">The cost of saving data has been plummeting, with the cost being halved about every 15 months. Now more and more data about individuals is being saved, both by corporations and governments. In his book, <i>Data and Goliath</i>, Bruce Schneier writes that worldwide, Google has the capacity to store 15 exabytes of data. To put it in context, one exabyte is 500 billion pages of text. Bruce also quotes the case of Max Schrems, an Austrian law student, who in 2011 demanded all his personal data from Facebook. After a two year legal battle, Facebook gave him a CD with 1200 pages of PDF. This is how much Facebook knows about you, and it does not forget because it is all saved.<br /> <br /> All this big data would be useless unless it can be utilised for decision making and this is where advances in AI have provided the breakthrough. Smart machines mine the data and detect trends, patterns, habits, ideology and desires. These personal characteristics of individuals are being used by corporations to send targeted advertisements to influence commercial decisions.<br /> <br /> The same technique is used in information warfare. On November 1, the US House Intelligence Committee released Facebook advertisements bought by Russian operatives to influence the 2016 elections. Washington Post wrote, “The ads made visceral appeals to voters concerned about illegal immigration...African American political activism, rising prominence of Muslims” among other issues. Senator Angus King said, “The strategy is to take a crack in our society and turn it into a chasm.”<br /> <br /> Data is the new oil and that is exactly how it is being traded and sold. In the absence of any legal provisions, companies and ‘data brokers’ are sharing and selling personal data. Can this personal data find its way to a hostile government? Last month, the US Army brought out that their troops in the Baltic had reported instances of cell phone hacking. However, more worrisome was the fact the hackers knew personal details of the soldiers. Direct threats against family members of the military can have a negative psychological impact during conflict.<br /> <br /> India has its share of political, social and ethnic differences, just as in many societies. In recent times these differences have been magnified as nationalism has taken centre stage. It is difficult to imagine why these fault lines will not be exploited by inimical forces as India enters the election mode in 2018. Looking at examples from the US and French elections, Brexit and the cyber battle during the Catalonia referendum, I think we have no option but to be prepared.<br /> <br /> The preparation for this war (and I do not use this word lightly) lies in three spheres — concepts, practices and structures.<br /> <br /> Conceptually, our current shortcoming is that we are viewing this issue through a technical prism rather than the broader spectrum of information warfare. CERT and NTRO can technically protect our critical infrastructure but they do not have an equal understanding of the human dimension, which is more strategic than scientific. The Americans, world leaders in information technology, have not been able to prevent a perceived subversion of their democratic process.<br /> <br /> Our practices need to improve. The security of personal data is a major concern. The Supreme Court has declared privacy as a fundamental right, but there are no privacy laws to back it up. Even data stored in India is not safe as the owners of our data are the giant technology companies, mostly based in the US and not under our legal control. In September 2017, it was reported that Google has quietly stopped challenging most search warrants from US judges in which the data requested is stored on overseas servers.<br /> <br /> A May 2017, report by the Centre for Internet and Society estimated that 135 million Aadhaar numbers could have been leaked from official portals. This was not due to a security breach but due to poor privacy practices.<br /> <br /> Our continued reliance on foreign hardware and software is extremely worrisome. There was clear evidence that Cisco systems had been back-doored by the American National Security Agency but the Indian military continues to procure hardware from Cisco. There is a similar story with Chinese equipment in our telecommunication and power sectors. An attempt to introduce an Indian operating system to replace Windows in the Army has been mired in controversy.<br /> <br /> In case of a targeted cyber attack on India, there is little we can do except issue advisories. The solutions will have to come from foreign manufactures or developers whose equipment we are using. There is an urgent need to give a fillip to developing indigenous solutions for our critical infrastructure.<br /> <br /> And finally, structures. An organisation to execute information warfare would have to be led by the Ministry of Defence, because the threat is mainly from external players. It would be a combination of military planners, specialists from the field of intelligence, government agencies, media and cyber warfare experts. Such an organisation does not currently exist, though the raising of the Cyber Command could fill this gap.<br /> <br /> In information warfare, the battlespace is the human mind. This is where the privacy of an individual intersects with national security. Fighting this battle will require a new paradigm in thought and action.<br /> <br /> <i><b>(The author is former Northern Commander, Indian Army, under whose leadership India carried out surgical strikes against Pakistan in 2016. Views are personal.)</b></i></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/news-18-lt-general-retd-ds-hooda-data-is-new-oil-and-human-mind-the-new-battlefield-india-must-wake-up-now'>http://editors.cis-india.org/internet-governance/news/news-18-lt-general-retd-ds-hooda-data-is-new-oil-and-human-mind-the-new-battlefield-india-must-wake-up-now</a>
</p>
No publisherAdminInternet GovernanceBig DataPrivacy2017-11-26T03:28:55ZNews ItemNew Media, personalisation and the role of algorithms
http://editors.cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms
<b>In his much acclaimed book, The Filter Bubble, Eli Pariser explains how personalisation of services on the web works and laments that they are creating individual bubbles for each user, which run counter to the idea of the Internet as an inherently open place. While Pariser’s book looks at the practices of various large companies providing online services, he briefly touches upon the role of new media such as search engines and social media portals in new curation. Building upon Pariser’s unexplored argument, this article looks at the impact of algorithmic decision-making and Big Data in the context of news reporting and curation.</b>
<em><br /></em>
<blockquote>
<div>
<div><em>Everything which bars freedom and fullness of communication sets up barriers that divide human beings into sets and cliques, into antagonistic sects and factions, and thereby undermines the democratic way of life. </em>—John Dewey</div>
</div>
</blockquote>
<p> Eli Pariser, in his book, The Filter Bubble,[1] refers to the scholarship by Walter Lippmann and John Dewey as integral to the evolution of the understanding of the democratic and ethical duties of the Fourth Estate. Lippmann was disillusioned by the role of newspapers in propaganda for the First World War. He responded with three books in quick succession — Liberty and the News,[2] Public Opinion[3] and The Phantom Public.[4] Lippmann brought attention the fact that the process of news-reporting was conducted through privately determined and unexamined standards. The failure of the Fourth Estate to perform its democratic functions, was, in the opinion of Lippmann, one of the prime factors responsible for the public not being an informed and rational entity. John Dewey, while rejecting Lippmann’s arguments that matters of public policy can only be determined by inside experts with training and education, did acknowledge the his critique of the media.</p>
<p>Pariser points to the creation of a wall between editorial decisionmaking and advertiser interests, as the eventual result of the Lippmann and Dewey debate. While accepting that this division between the financial and reporting sides of media houses has not been always observed, Pariser emphasises that the fact that the standard exists is important.[5] Unlike traditional media, the new media which relies on algorithmic decision-making for personalisation is not subject to the same standards which try to mitigate the influence of commercial interests on editorial decisions while performing many of the same functions as the traditional media.[6] </p>
<h3>How personalisation algorithms work</h3>
<p dir="ltr">Kevin Slavin, at his famous talk in the TEDGLobal Conference, characterised algorithms as “maths that computers use to decide stuff” and that it was infiltrating every aspect of our lives.[7] According to Slavin’s view, algorithms can be seen as control technologies and shape our world constantly through media and information systems, dynamically modifying content and function through these programmed routines. Search engines and social media platforms perpetually rank user-generated content through algorithms.[8]</p>
<p>Personalisation technologies have various advantages. It translates into more relevant content, which for service providers means more clicks and revenue and for consumer, less time spent on finding the content.[9] However, it also leads to privacy compromise, lack of control and reduced individual capability.[10] Search engines like Google use the famous PageRank algorithm, which combined with geographical location and previous searches yields most relevant search results.[11] PageRank algorithm uses various real time variables dependent on both voluntary and involuntary user inputs. These variables include number of clicks, number of occurrences of the key terms and number of references by other credible pages etc. This data in turn determines the order of pages in search results and influences the way we perceive, understand and analyse information.[12] Maps showing real time traffic information retrieve data from laser and infrared sensors alongside the road and from information from devices of users. Once this real time data is combined with historical trends, these maps recommend rout to every user, hence influencing the traffic patterns.[13]</p>
<p>Even though this phenomenon of personalization may appears to be new, it has been prevalent in the society for ages.[14] The history of mass media culture clearly shows personalization has always been a method to increase market, market reach and customer satisfaction.[15] Newspapers have sections dedicated to special topics, radio and TV have channels dedicated to different interest groups, age groups and consumers.[16] These personalised sections in a newspaper and personalised channels on radio and television don’t just provide greater satisfaction to the readers or listeners or consumers, they also provide targeted advertisement space for the advertisers and content developers. However, digital footprints and mass collection of data have made this phenomenon much more granular and detailed. Geographical location of an individual can tell a lot about their community, their culture and other important traits local to a community.[17] This data further assists in personalisation. Current developments in technology not only help in better collection of data about personal preferences but also help in better personalisation.</p>
<p>Pariser mentions three ways in which the personalization technologies of this day are different from those of the past. First, for the very first time, individuals are alone in the filter bubble. While in traditional forms of personalisation, there were various individuals who shared the same frame of reference, now there is a separate sets of filters governing the dissemination of content to each individual.[18] Second, the personalisation technologies are entirely invisible now, and there is little that consumers can do to control or modify them.[19] Third, often the decision to be subject to these personalisation technologies is not an informed choice. A good example of this would be an individual’s geographical location.[20]</p>
<h3>The neutrality of New Media?</h3>
<p dir="ltr">More and more, we have noticed personalisation technologies having an impact on how we consume news on the Internet. Google News, Facebook’s News Feed which tries to put together a dynamic feed for both personal and global stories, and Twitter’s trending hashtag feature, have brought forward these services are key drivers of an emerging news ecosystem. Initially, this new media was hailed as a natural consequence of the Internet which would enable greater public participation, allow journalists to find more stories and engage with the readers directly. An illustration of the same could be seen in the way Internet based news media and social networking websites behaved in the aftermath of Israel’s attacks on a United Nations run school in Gaza strip. While much of the international Internet media covered the story, Israel’s home media did not cover the story. The only exception to this was the liberal Israeli news website Ha’aretz.[21] Network graph details of Twitter, for a few days immediately after the incident clearly show the social media manifestation of the event in the personalised cyberspace. It is clearly visible that when most of the word was re-tweeting news of this heinous act of Israel, Israeli’s hardly re-tweeted this news. In fact they were busty re-tweeting the news of rocket attacks on Israel.[22]</p>
<p>The use of social media in newsmaking was hailed by many scholars as symptomatic of the decentralisation characteristic of the Internet. It has been seen as movement towards greater grassroots participation by negating the ‘gatekeeping’ role traditionally played by editors. Thomas Poell and José van Dijck punch holes in theory of social media and other online technologies as mere facilitators of user participation and translators of user preferences through Big Data analytics.[23] They quote T. Gillespie’s work which talks of the narrative of these online services as platforms which are “open, neutral, egalitarian and progressive support for activity.”[24]</p>
<p>Pedro Domingos calls the overwhelming number of choices as the defining problem of the information age, and machine learning and data analytics as the largest part of this solution.[25] The primary function of algorithmic decision making in the context of consumption of content is to narrow down the choices. Domingos is more optimistic about the impact of these technologies, and he says “last step of the decision is usually still for humans to make, but learners intelligently reduce the choices to something a human can manage.”[26] On the other hand, Pariser is more circumspect about the coercive result of machine learning algorithms. Whichever way we lean, we have to accept that a large part of personalisation algorithms is to select and prioritize content by categorising it on the basis of relevance and popularity. </p>
<p>Poell and van Dijck call this a new knowledge logic which in effect replaces human judgement (as, earlier exercised by editors) to some kind of proxy decisionmaking based on data. Their main thesis is that there is little evidence to suggest that the latter is more democratic than former and creates new problems of its own. They go on to compare the practices of various services including Facebook’s new graph and Twitter’s trending topic, and conclude that they prioritise breaking news stories over other kinds of content.[27] For instance, the algorithm for the trending topics depends not on the volume but the velocity of the tweets with the hashtag or term. It could be argued that given this predilection, the algorithms will rarely prefer complex content. If we go by Lippmann and Dewey’s idea that the role of the Fourth Estate is to inform public debate and accountability of those in positions of power, this aspect of Big Data algorithms does not correspond with this role.</p>
<h3>Quantified Audience</h3>
<p dir="ltr">Another aspect of use of Big Data and algorithms in New Media that requires attention is that the networked infrastructure enables a quantified audience. C W Anderson who has studied newsroom practices in the US looked at role played by audience quantification and rationalization in shifting newswork practices. He concluded that more and more, journalists are less autonomous in their news decisions and increasingly reliant on audience metrics as a supplement to news judgment.[28] Poell and van Dijck review the the practices by some leading publications such a New York Times, L.A. Times and Huffington Post, and degree to which audience metrics dictates editorial decisions. While New York Times seems to prioritise content on their social media portals based on expectation of spike in user traffic, L.A. Times goes one step further by developing content specifically aimed towards promoting greater social participation. Neither of these practices though compare to the reliance on SEO and SMO strategies of web-born news providers like Huffington Post. They have traffic editors who trawl the Internet for trending topics and popular search terms, the feedback from them dictates the content creation.[29]</p>
<h3>Conclusion</h3>
<p dir="ltr">The above factors demonstrate that the idea of New Media leading to the Fourth Estate performing its democratic functions does not take into account the actual practices. This idea is based on the erroneous assumption that technology, in general and algorithms, in particular are neutral. While the emergence of New Media might have reduced the gatekeeping role played by the editors, its strong prioritisation of content that will be popular reduce the validity of arguments that it leads to more informed public discussion. As Pariser said, the traditional media scores over the New Media inasmuch as there is an existence of a standard of division between editorial decisionmaking and advertiser interest. While this standard is flouted by media houses all the time, it exists as a metric to aspire to and measure service providers against. The New Media performs many of the same functions and maybe it is time to evolve some principles and ethical standards that take into account the need for it to perform these democratic functions.</p>
<h3>Endnotes </h3>
<p class="normal"><sup><sup>[1]</sup></sup> Eli Pariser, The Filter Bubble: What the Internet is
hiding from you (The Penguin Press, New York, 2011) </p>
<p dir="ltr"><span class="MsoFootnoteReference"><span class="MsoFootnoteReference">[2]</span></span> Walter Lippmann, Liberty and News (Harcourt, Brace
and Howe, New York 1920) available at<a href="https://archive.org/details/libertyandnews01lippgoog">https://archive.org/details/libertyandnews01lippgoog</a></p>
<p class="normal"><sup><sup>[3]</sup></sup> Walter Lippmann, Public Opinion (Harcourt, Brace and
Howe, New York 1920) available at <a href="http://xroads.virginia.edu/~Hyper2/CDFinal/Lippman/cover.html">http://xroads.virginia.edu/~Hyper2/CDFinal/Lippman/cover.html</a></p>
<p class="normal"><sup><sup>[4]</sup></sup> Walter Lippmann, The Phantom Public (Transaction
Publishers, New York, 1925)</p>
<p class="normal"><sup><sup>[5]</sup></sup> <em>Supra</em> Note
1 at 35.</p>
<p class="normal"><sup><sup>[6]</sup></sup> <em>Supra</em> Note
1 at 36.</p>
<p class="normal"><sup><sup>[7]</sup></sup> <a href="https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world/transcript?language=en">https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world/transcript?language=en</a></p>
<p class="normal"><sup><sup>[8]</sup></sup> Fenwick McKelvey, “Algorithmic Media Need Democratic
Methods: Why Publics Matter”, available at <a href="http://www.fenwickmckelvey.com/wp-content/uploads/2014/11/2746-9231-1-PB.pdf">http://www.fenwickmckelvey.com/wp-content/uploads/2014/11/2746-9231-1-PB.pdf</a>.</p>
<p class="normal"><sup><sup>[9]</sup></sup> <a href="http://mashable.com/2011/06/03/filters-eli-pariser/#9tIHrpa_9Eq1">http://mashable.com/2011/06/03/filters-eli-pariser/#9tIHrpa_9Eq1</a></p>
<p class="normal"><sup><sup>[10]</sup></sup> Helen Ashman, Tim Brailsford, Alexandra Cristea, Quan
Z Sheng, Craig Stewart, Elaine Torns and Vincent Wade, “The ethical and social
implications of personalization technologies for e-learning” available at <a href="http://www.sciencedirect.com/science/article/pii/S0378720614000524">http://www.sciencedirect.com/science/article/pii/S0378720614000524</a>.</p>
<p class="normal"><sup><sup>[11]</sup></sup> Sergey Brin and Lawrence Page, “The Anatomy of a
Large-Scale Hypertextual Web Search Engine” available at <a href="http://infolab.stanford.edu/pub/papers/google.pdf">http://infolab.stanford.edu/pub/papers/google.pdf</a>.</p>
<p class="normal"><sup><sup>[12]</sup></sup> Ian Rogers, “The Google Pagerank Algorithm and How It
Works” available at <a href="http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm">http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm</a>.</p>
<p class="normal"><sup><sup>[13]</sup></sup> Trygve Olson and Terry Nelson, “The Internet’s Impact
on Political Parties and Campaigns”, available at <a href="http://www.kas.de/wf/doc/kas_19706-544-2-30.pdf?100526130942">http://www.kas.de/wf/doc/kas_19706-544-2-30.pdf?100526130942</a>.</p>
<p class="normal"><sup><sup>[14]</sup></sup> Ian Witten, “Bias, privacy and and personalisation on
the web”, available at <a href="http://www.cs.waikato.ac.nz/~ihw/papers/07-IHW-Bias,privacyonweb.pdf">http://www.cs.waikato.ac.nz/~ihw/papers/07-IHW-Bias,privacyonweb.pdf</a>.</p>
<p class="normal"><sup><sup>[15]</sup></sup> <em>Supra</em> Note
1 at 10.</p>
<p class="normal"><sup><sup>[16]</sup></sup> <a href="https://www.americanpressinstitute.org/publications/reports/survey-research/social-demographic-differences-news-habits-attitudes/">https://www.americanpressinstitute.org/publications/reports/survey-research/social-demographic-differences-news-habits-attitudes/</a></p>
<p class="normal"><sup><sup>[17]</sup></sup> Charles Heatwole, “Culture: A Geographical Perspective”
available at <a href="http://www.p12.nysed.gov/ciai/socst/grade3/geograph.html">http://www.p12.nysed.gov/ciai/socst/grade3/geograph.html</a>.</p>
<p class="normal"><sup><sup>[18]</sup></sup> <em>Supra</em> Note
1 at 10.</p>
<p class="normal"><sup><sup>[19]</sup></sup> <em>Id</em>.</p>
<p class="normal"><sup><sup>[20]</sup></sup> <em>Supra</em> Note
1 at 11.</p>
<p class="normal"><sup><sup>[21]</sup></sup> Paul Mason, “Why Israel is losing the social media
war over Gaza?” available at <a href="http://blogs.channel4.com/paul-mason-blog/impact-social-media-israelgaza-conflict/1182">http://blogs.channel4.com/paul-mason-blog/impact-social-media-israelgaza-conflict/1182</a>.</p>
<p class="normal"><sup><sup>[22]</sup></sup> Gilad Lotan, Israel, Gaza, War & Data: Social
Networks and the Art of Personalizing Propaganda available at <a href="http://www.huffingtonpost.com/entry/israel-gaza-war-social-networks-data_b_5658557.html">www.huffingtonpost.com/entry/israel-gaza-war-social-networks-data_b_5658557.html</a></p>
<p class="normal"><sup><sup>[23]</sup></sup> Thomas Poell and José van Dijck, “Social Media and
Journalistic Independence” in Media Independence: Working with Freedom or
Working for Free?, edited by James Bennett & Niki Strange. (Routledge,
London, 2015)</p>
<p class="normal"><sup><sup>[24]</sup></sup> T Gillespie, “The politics of ‘platforms,” in New
Media & Society (Volume 12, Issue 3).</p>
<p class="normal"><sup><sup>[25]</sup></sup> Pedro Domingos, The Master Algorithm: How the quest
for the ultimate learning machine will re-make the world (Basic Books, New
York, 2015) at 38.</p>
<p class="normal"><sup><sup>[26]</sup></sup> <em>Ibid</em> at 40.</p>
<p class="normal"><sup><sup>[27]</sup></sup> <em>Supra</em> Note
23.</p>
<p class="normal"><sup><sup>[28]</sup></sup> C W Anderson, Between creative and quantified
audiences: Web metrics and changing patterns of newswork in local US newsrooms,
available at <a href="https://www.academia.edu/10937194/Between_Creative_And_Quantified_Audiences_Web_Metrics_and_Changing_Patterns_of_Newswork_in_Local_U.S._Newsrooms">https://www.academia.edu/10937194/Between_Creative_And_Quantified_Audiences_Web_Metrics_and_Changing_Patterns_of_Newswork_in_Local_U.S._Newsrooms</a></p>
<p dir="ltr">
<sup><sup>[29]</sup></sup> <em>Supra </em>Note 23.</p>
<p dir="ltr"><span id="docs-internal-guid-24b4db2a-a606-d425-16ff-1d76b980367d"><br /></span></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms'>http://editors.cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms</a>
</p>
No publisheramberHuman RightsBig DataInternet GovernanceMachine LearningAlgorithmsNew Media2017-01-16T07:20:52ZBlog EntryNASA 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 DataOpenness2015-04-27T01:08:27ZEventMediaNama - #NAMAprivacy: The Future of User Data (Delhi, Sep 6)
http://editors.cis-india.org/internet-governance/news/medianama-namaprivacy-the-future-of-user-data-delhi-sep-6
<b>MediaNama is hosting a full day conference on "the future of user data in India", on the 6th of September 2017, which is particularly significant given the recent Supreme Court ruling on the fundamental right to privacy, and two government consultations: one at the TRAI, and another at MEITY. This discussion is supported by Facebook, Google, and Microsoft. Sumandro Chattapadhyay, Research Director, will participate as a speaker in the session titled "regulating storage, sharing and transfer of data."</b>
<p> </p>
<h4>Details</h4>
<p>Time: September 6th 2017, 9 am to 4:30 pm</p>
<p>Venue: Gulmohar Hall, India Habitat Centre, Lodhi Road (please enter from Gate #3)</p>
<p>Agenda: <a href="https://www.medianama.com/2017/08/223-agenda-namaprivacy-future-of-user-data/">https://www.medianama.com/2017/08/223-agenda-namaprivacy-future-of-user-data/</a></p>
<h4>Announced Speakers</h4>
<ul><li>Chinmayi Arun, Centre for Communication Governance at NLU Delhi</li>
<li>Malavika Raghavan, IFMR Finance Foundation</li>
<li>Renuka Sane, NIPFP</li>
<li>Smitha Krishna Prasad, Centre for Communication Governance at NLU Delhi</li>
<li>Ananth Padmanabhan, Carnegie India</li>
<li>Avinash Ramachandra, Amazon</li>
<li>Hitesh Oberoi, Naukri</li>
<li>Jochai Ben-Avie, Mozilla</li>
<li>Mrinal Sinha, Mobikwik</li>
<li>Murari Sreedharan, Bankbazaar</li>
<li>Sumandro Chattapadhyay, Centre for Internet and Society</li></ul>
<h4>Facilitators</h4>
<ul><li>Saikat Datta, Asia Times Online</li>
<li>Shashidar KJ, MediaNama</li>
<li>Nikhil Pahwa, MediaNama</li></ul>
<h4>Attendees</h4>
<p>We have confirmed 140+ attendees from: Adobe, Amber Health, Amazon, APCO Worldwide, Bank Bazaar, Bloomberg-Quint, Blume Ventures, Broadband India Forum, Business Standard, BuzzFeed News, CCOAI, CEIP, Change Alliance, Chase India, CIS, CNN News18, DEF, Deloitte, DNA, DSCI, E2E Networks, British High Commission, Eurus Network Services, FICCI, Firefly Networks, Flipkart, Forrester Research, Fortumo, DoT, MEITY, IAMAI, IBM, ICRIER, IFMR Finance Foundation, IIMC, Indian Law Institute, Indic Project, Info Edge, ISPAI, IT for Change, ITU-APT, Jamia Millia Islamia, Jindal Global Law School, Mimir Technologies, Mozilla, Newslaundry, NIPFP, Nishith Desai Associates, NIXI, NLU-Delhi, ORF, Paytm, PLR Chambers, PRS Legislative Research, Publicis Groupe, Quartz India, Reliance Jio, Reuters, Saikrishna & Associates, Scroll.in, SFLC.in, Spectranet, The Economics Times, The Indian Express, The Times of India, The Wire, Times Internet, Twitter, and more.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/medianama-namaprivacy-the-future-of-user-data-delhi-sep-6'>http://editors.cis-india.org/internet-governance/news/medianama-namaprivacy-the-future-of-user-data-delhi-sep-6</a>
</p>
No publishersumandroBig DataDigital EconomyPrivacyInternet GovernanceData GovernanceData ProtectionDigital Rights2017-09-05T10:22:12ZBlog EntryList of Recommendations on the Aadhaar Bill, 2016 - Letter Submitted to the Members of Parliament
http://editors.cis-india.org/internet-governance/blog/list-of-recommendations-on-the-aadhaar-bill-2016
<b>On Friday, March 11, the Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. The Bill was introduced as a money bill and there was no public consultation to evaluate the provisions therein even though there are very serious ramifications for the Right to Privacy and the Right to Association and
Assembly. Based on these concerns, and numerous others, we submitted an initial list of recommendations to the Members of Parliaments to highlight the aspects of the Bill that require immediate attention.</b>
<p> </p>
<h4>Download the submission letter: <a href="https://github.com/cis-india/website/raw/master/docs/CIS_Aadhaar-Bill-2016_List-of-Recommendations_2016.03.16.pdf">PDF</a>.</h4>
<p> </p>
<h3>Text of the Submission</h3>
<p>On Friday, March 11, the Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. The Bill was introduced as a money bill and there was no public consultation to evaluate the provisions therein even though there are very serious ramifications for the Right to Privacy and the Right to Association and Assembly. The Bill has made it compulsory for all Indian to enroll for Aadhaar in order to receive any subsidy, benefit, or service from the Government whose expenditure is incurred from the Consolidate Fund of India. Apart from the issue of centralisation of the national biometric database leading to a deep national vulnerability, the Bill also keeps unaddressed two serious concerns regarding the technological framework concerned:</p>
<ul><li><strong>Identification without Consent:</strong> Before the Aadhaar project it was not possible for the Indian government or any private entity to identify citizens (and all residents) without their consent. But biometrics allow for non-consensual and covert identification and authentication. The only way to fix this is to change the technology configuration and architecture of the project. The law cannot be used to correct the problems in the technological design of the project.<br /><br /></li>
<li><strong>Fallible Technology:</strong> The Biometrics Standards Committee of UIDAI has acknowledged the lack of data on how a biometric authentication technology will scale up where the population is about 1.2 billion. The technology has been tested and found feasible only for a population of 200 million. Further, a report by 4G Identity Solutions estimates that while in any population, approximately 5% of the people have unreadable fingerprints, in India it could lead to a failure to enroll up to 15% of the population. For the current Indian population of 1.2 billion the expected proportion of duplicates is 1/121, a ratio which is far too high. <strong>[1]</strong></li></ul>
<p>Based on these concerns, and numerous others, we sincerely request you to ensure that the Bill is rigorously discussed in Rajya Sabha, in public, and, if needed, also by a Parliamentary Standing Committee, before considering its approval and implementation. Towards this, we humbly submit an initial list of recommendations to highlight the aspects of the Bill that require immediate attention:</p>
<ol><li><strong>Implement the Recommendations of the Shah and Sinha Committees:</strong> The report by the Group of Experts on Privacy chaired by the Former Chief Justice A P Shah <strong>[2]</strong> and the report by the Parliamentary Standing Committee on Finance (2011-2012) chaired by Shri Yashwant Sinha <strong>[3]</strong> have suggested a rigorous and extensive range of recommendations on the Aadhaar / UIDAI / NIAI project and the National Identification Authority of India Bill, 2010 from which the majority sections of the Aadhaar Bill, 2016, are drawn. We request that these recommendations are seriously considered and incorporated into the Aadhaar Bill, 2016.<br /><br /></li>
<li><strong>Authentication using the Aadhaar number for receiving government subsidies, benefits, and services cannot be made mandatory:</strong> Section 7 of the Aadhaar Bill, 2016, states that authentication of the person using her/his Aadhaar number can be made mandatory for the purpose of disbursement of government subsidies, benefits, and services; and in case the person does not have an Aadhaar number, s/he will have to apply for Aadhaar enrolment. This sharply contradicts the claims made by UIDAI earlier that the Aadhaar number is “optional, and not mandatory”, and more importantly the directive given by the Supreme Court (via order dated August 11, 2015). The Bill must explicitly state that the Aadhaar number is only optional, and not mandatory, and a person without an Aadhaar number cannot be denied any democratic rights, and public subsidies, benefits, and services, and any private services.<br /><br /></li>
<li><strong>Vulnerabilities in the Enrolment Process:</strong> The Bill does not address already documented issues in the enrolment process. In the absence of an exhaustive list of information to be collected, some Registrars are permitted to collect extra and unnecessary information. Also, storage of data for elongated periods with Enrollment agencies creates security risks. These vulnerabilities need to be prevented through specific provisions. It should also be mandated for all entities including the Enrolment Agencies, Registrars, CIDR and the requesting entities to shift to secure system like PKI based cryptography to ensure secure method of data transfer.<br /><br /></li>
<li><strong>Precisely Define and Provide Legal Framework for Collection and Sharing of Biometric Data of Citizens:</strong> The Bill defines “biometric information” is defined to include within its scope “photograph, fingerprint, iris scan, or other such biological attributes of an individual.” This definition gives broad and sweeping discretionary power to the UIDAI / Central Government to increase the scope of the term. The definition should be exhaustive in its scope so that a legislative act is required to modify it in any way.<br /><br /></li>
<li><strong>Prohibit Central Storage of Biometrics Data:</strong> The presence of central storage of sensitive personal information of all residents in one place creates a grave security risk. Even with the most enhanced security measures in place, the quantum of damage in case of a breach is extremely high. Therefore, storage of biometrics must be allowed only on the smart cards that are issued to the residents.<br /><br /></li>
<li><strong>Chain of Trust Model and Audit Trail:</strong> As one of the objects of the legislation is to provide targeted services to beneficiaries and reduce corruption, there should be more accountability measures in place. A chain of trust model must be incorporated in the process of enrolment where individuals and organisations vouch for individuals so that when a ghost is introduced someone has can be held accountable blame is not placed simply on the technology. This is especially important in light of the questions already raised about the deduplication technology. Further, there should be a transparent audit trail made available that allows public access to use of Aadhaar for combating corruption in the supply chain.<br /><br /></li>
<li><strong>Rights of Residents:</strong> There should be specific provisions dealing with cases where an individual is not issued an Aadhaar number or denied access to benefits due to any other factor. Additionally, the Bill should make provisions for residents to access and correct information collected from them, to be notified of data breaches and legal access to information by the Government or its agencies, as matter of right. Further, along with the obligations in Section 8, it should also be mandatory for all requesting entities to notify the individuals of any changes in privacy policy, and providing a mechanism to opt-out.<br /><br /></li>
<li><strong>Establish Appropriate Oversight Mechanisms:</strong> Section 33 currently specifies a procedure for oversight by a committee, however, there are no substantive provisions laid down that shall act as the guiding principles for such oversight mechanisms. The provision should include data minimisation, and “necessity and proportionality” principles as guiding principles for any exceptions to Section 29.<br /><br /></li>
<li><strong>Establish Grievance Redressal and Review Mechanisms:</strong> Currently, there are no grievance redressal mechanism created under the Bill. The power to set up such a mechanism is delegated to the UIDAI under Section 23 (2) (s) of the Bill. However, making the entity administering a project, also responsible for providing for the frameworks to address the grievances arising from the project, severely compromises the independence of the grievance redressal body. An independent national grievance redressal body with state and district level bodies under it, should be set up. Further, the NIAI Bill, 2010, provided for establishing an Identity Review Committee to monitor the usage pattern of Aadhaar numbers. This has been removed in the Aadhaar Bill 2016, and must be restored.</li></ol>
<p> </p>
<h3>Endnotes</h3>
<p><strong>[1]</strong> See: <a href="http://cis-india.org/internet-governance/blog/Flaws_in_the_UIDAI_Process_0.pdf.">http://cis-india.org/internet-governance/blog/Flaws_in_the_UIDAI_Process_0.pdf</a>.</p>
<p><strong>[2]</strong> See: <a href="http://planningcommission.nic.in/reports/genrep/rep_privacy.pdf">http://planningcommission.nic.in/reports/genrep/rep_privacy.pdf</a>.</p>
<p><strong>[3]</strong> See: <a href="http://164.100.47.134/lsscommittee/Finance/15_Finance_42.pdf">http://164.100.47.134/lsscommittee/Finance/15_Finance_42.pdf</a>.</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/list-of-recommendations-on-the-aadhaar-bill-2016'>http://editors.cis-india.org/internet-governance/blog/list-of-recommendations-on-the-aadhaar-bill-2016</a>
</p>
No publisherAmber Sinha, Sumandro Chattapadhyay, Sunil Abraham, and Vanya RakeshUIDBig DataPrivacyInternet GovernanceFeaturedDigital IndiaAadhaarBiometricsHomepage2016-03-21T08:50:09ZBlog 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 ItemIs 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 EntryIdentity of the Aadhaar Act: Supreme Court and the Money Bill Question
http://editors.cis-india.org/internet-governance/blog/identity-of-the-aadhaar-act-supreme-court-and-the-money-bill-question
<b>A writ petition has been filed by former Union minister Jairam Ramesh on April 6 challenging the constitutionality and legality of the treatment of this Act as a money bill. The Supreme Court heard the matter on April 25 and invited the Union government to present its view. It is our view that the Supreme Court can not only review the Lok Sabha speaker’s decision, but should also ask the government to draft the Aadhaar Bill again, this time with greater parliamentary and public deliberation. Vanya Rakesh and Sumandro Chattapadhyay wrote this article on The Wire.</b>
<p> </p>
<p>Published by and cross-posted from <a href="http://thewire.in/2016/05/09/identity-of-the-aadhaar-act-supreme-court-and-the-money-bill-question-34721/">The Wire</a>.</p>
<hr />
<p>The Aadhaar Act 2016, passed in the Lok Sabha on March 16, 2016, <a href="http://www.thehindu.com/news/national/opposition-picks-holes-in-aadhaar-bill/article8361213.ece">faced opposition</a> ever since it was tabled in parliament. In particular, the move to introduce it as a money bill has been vehemently challenged on grounds of this being an attempt to bypass the Rajya Sabha completely. <a href="http://www.thehindu.com/news/national/jairam-ramesh-moves-supreme-court-against-treating-aadhaar-bill-as-money-bill/article8446997.ece">A writ petition has been filed by former Union minister Jairam Ramesh on April 6</a> challenging the constitutionality and legality of the treatment of this Act as a money bill. The Supreme Court heard the matter on April 25 and invited the Union government to present its view.</p>
<p>It is our view that the Supreme Court can not only review the Lok Sabha speaker’s decision, but should also ask the government to draft the Aadhaar Bill again, this time with greater parliamentary and public deliberation.</p>
<h3>The money bill question</h3>
<p>M.R. Madhavan <a href="http://indianexpress.com/article/opinion/columns/aadhaar-bill-money-bill-name-of-the-bill-2754080/">has argued</a> that the Aadhaar Act contains matters other than “only” those incidental to expenditure from the consolidated fund, as it establishes a biometrics-based unique identification number for beneficiaries of government services and benefits, but also allows the number to be used for other purposes beyond service delivery. While Pratap Bhanu Mehta <a href="http://indianexpress.com/article/opinion/columns/privacy-after-aadhaar-money-bill-rajya-sabha-upa/">calls this a subversion</a> of “the spirit of the constitution”, P.D.T. Achary, former secretary general of the Lok Sabha, <a href="http://indianexpress.com/article/opinion/columns/show-me-the-money-4/">expressed concern</a> about the attempts to pass off financial bills like Aadhaar as money bills as a means to <a href="http://www.thehindu.com/opinion/lead/circumventing-the-rajya-sabha/article7531467.ece">circumvent</a> and erode the supervisory role of the Rajya Sabha. Arvind Datar has further emphasised that when the primary purpose of a bill is not governed by Article 110(1), then certifying it as a money bill is <a href="http://indianexpress.com/article/opinion/columns/making-a-money-bill-of-it/">an unconstitutional act</a>.</p>
<p>Article 110(1) of the Constitution identifies a bill as a money bill if it contains “only” provisions dealing with the following matters, or those incidental to them:</p>
<ol>
<li>imposition and regulation of any tax,</li>
<li>financial obligations undertaken by Indian Government,</li>
<li>payment into or withdrawal from the Consolidated Fund of India (CFI) or Contingent Fund of India,</li>
<li>appropriation of money and expenditure charged on the CFI or receipt, and</li>
<li>custody, issue or audit of money into CFI or public account of India.</li></ol>
<p>However, the link of the Act with the Consolidated Fund of India is rather tenuous, since it depends on the Union or state governments declaring a certain subsidy to be available upon verification of the Aadhaar number. The objectives and validity of the Act would not actually change if the Aadhaar number no longer was directly connected to the delivery of services. The use of the word “if” in section 7 explicitly leaves scope for a situation where the government does not declare an Aadhaar verification as necessary for accessing a subsidy. In such a scenario, the Act will still be valid but without any formal connection with any charges on the Consolidated Fund of India.</p>
<h3>A case of procedural irregularity?</h3>
<p>The constitution of India borrows the idea of providing the speaker with the authority to certify a bill as money bill from British law, but operationalises it differently. In the UK, though the speaker’s certificate on a money bill is <a href="https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/480476/Money_Bills__12_Nov_2015___accessible_PDF_.pdf">conclusive</a> for all purposes under section 3 of the Parliament Act 1911, the speaker is <a href="http://www.publications.parliament.uk/pa/ld201011/ldselect/ldconst/97/9703.htm">required to consult</a> two senior members, usually one from either side of the house, appointed by the committee from amongst those senior MPs who chair general committees. In India, the speaker makes the decision on her own.</p>
<p>Although article 110 (3) of the Indian constitution states that the decision of the speaker of the Lok Sabha shall be final in case a question arises regarding whether a bill is a money bill or not, this does not restrict the Supreme Court from entertaining and hearing a petition contesting the speaker’s decision. As the Aadhaar Act was introduced in the Lok Sabha as a money bill even though it does not meet the necessary criteria for such a classification, this treatment of the bill may be considered as an instance of <em>procedural irregularity</em>.</p>
<p>There is ample jurisprudence on what happens when the Supreme Court’s power of judicial review comes up against Article 122 – which states that the validity of any proceeding in the parliament can (only) be called into question on the grounds of procedural irregularities. In the crucial judgment of <a href="https://indiankanoon.org/doc/1757390/"><em>Raja Ram Pal vs Hon’ble Speaker, Lok Sabha and Others</em></a> (2007), the court evaluated the scope of judicial review and observed that although parliament is supreme, unlike Britain, proceedings which are found to suffer from substantive illegality or unconstitutionality, cannot be held protected from judicial scrutiny by article 122, as opposed to mere irregularity. Deciding upon the scope for judicial intervention in respect of exercise of power by the speaker, in <a href="https://indiankanoon.org/doc/1686885/"><em>Kihoto Hollohan vs Zachillhu and Ors.</em></a> (1992), the Supreme Court held that though the speaker of the house holds a pivotal position in a parliamentary democracy, the decision of the speaker (while adjudicating on disputed disqualification) is subject to judicial review that may look into the correctness of the decision.</p>
<p>Several past decisions of the Supreme Court discuss how the tests of legality and constitutionality help decide whether parliamentary proceedings are immune from judicial review or not. In <a href="https://indiankanoon.org/doc/1249806/"><em>Ramdas Athawale vs Union of India</em></a> (2010), the case of <a href="https://indiankanoon.org/doc/638013/"><em>Keshav Singh vs Speaker, Legislative Assembly</em></a> (1964) was referred to, in which the judges had unequivocally upheld the judiciary’s power to scrutinise the actions of the speaker and the houses. It was observed that if the parliamentary procedure is illegal and unconstitutional, it would be open to scrutiny in a court of law and could be a ground for interference by courts under <a href="https://indiankanoon.org/doc/981147/">Article 32</a>, though the immunity from judicial interference under this article is confined to matters of irregularity of procedure. These observations were reiterated in <a href="https://indiankanoon.org/docfragment/108219590/?formInput=lokayukta"><em>Mohd. Saeed Siddiqui vs State of Uttar Pradesh</em></a> (2014) and <a href="https://indiankanoon.org/doc/199851373/"><em>Yogendra Kumar Jaiswal vs State of Bihar</em></a> (2016).</p>
<p>Thus, the decision of the Lok Sabha speaker to pass and certify a bill as a money bill is definitely not immune from judicial review. Additionally, the Supreme Court has the power to issue directions, orders or writs for enforcement of rights under Article 32 of the constitution, therefore, allowing the judiciary to decide upon the manner of introducing the Aadhaar Act in parliament.</p>
<h3>National implications demand public deliberation</h3>
<p>As the provisions of the Aadhaar Act have <a href="http://indianexpress.com/article/opinion/columns/privacy-after-aadhaar-money-bill-rajya-sabha-upa/">far reaching implications</a> for the fundamental and constitutional rights of Indian citizens, the Supreme Court should look into the matter of its identification and treatment as a money bill and whether such decisions lead to the thwarting of legislative and procedural justice.</p>
<p>The Supreme Court may also take this opportunity to reflect on the very decision making process for classification of bills in general. As <a href="http://www.thehoot.org/media-watch/law-and-policy/aadhar-why-classification-matters-in-law-making-9281">Smarika Kumar argues</a>, experience with the Aadhaar Act reveals a structural concern regarding this classification process, which may have substantial implications in terms of undermining public and parliamentary deliberative processes. This “trend,” as <a href="http://indianexpress.com/article/opinion/columns/making-a-money-bill-of-it/">Arvind Datar notes</a>, of limiting legislative discussions and decisions of national importance within the space of the Lok Sabha must be swiftly curtailed.</p>
<p>Apart from deciding upon the legality of the nature of the bill, it is vital that the apex court ask the government to categorically respond to the concerns red-flagged by the <a href="http://164.100.47.134/lsscommittee/Finance/15_Finance_42.pdf">Standing Committee on Finance</a>, which had taken great exception to the continued collection of data and issuance of Aadhaar numbers in its report, and to the recommendations <a href="http://thewire.in/2016/03/16/three-rajya-sabha-amendments-that-will-shape-the-aadhaar-debate-24993/">passed in the Rajya Sabha recently</a>. Further, the repeated violation of the Supreme Court’s interim orders – that the Aadhaar number cannot be made mandatory for availing benefits and services – in contexts ranging from <a href="http://www.caravanmagazine.in/vantage/how-get-married-without-aadhaar-number">marriages</a> to the <a href="http://www.thehindu.com/news/national/payment-denied-for-nrega-workers-without-uidai-cards-in-jharkhand/article5674969.ece">guaranteed work programme</a> should also be addressed and responses sought from the Union government.</p>
<p>Evidently, the substantial implications of the Aadhaar Act for national security and fundamental rights of citizens, primarily privacy and data security, make it imperative to conduct a duly balanced public deliberation process, both within and outside the houses of parliament, before enacting such a legislation.</p>
<p> </p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/identity-of-the-aadhaar-act-supreme-court-and-the-money-bill-question'>http://editors.cis-india.org/internet-governance/blog/identity-of-the-aadhaar-act-supreme-court-and-the-money-bill-question</a>
</p>
No publisherVanya Rakesh and Sumandro ChattapadhyayUIDBig DataPrivacyInternet GovernanceAadhaar2016-05-09T11:52:44ZBlog EntryGlobal Technology Summit 2017
http://editors.cis-india.org/internet-governance/news/global-technology-summit-2017
<b>The 2017 Global Technology Summit will take place on December 7 and 8, 2017 at the Hotel Leela Palace, Bangalore. Sunil Abraham is a speaker at the event.</b>
<p style="text-align: justify; ">Link to the original published by Carnegie <a class="external-link" href="http://carnegieindia.org/2017/12/08/global-technology-summit-2017-event-5656?mkt_tok=eyJpIjoiTjJKbFlXWTBaakV3TVRVMSIsInQiOiJ1YkRmVHZHd2h2bVFOTzNEQm94YzRBYUtrWjFwNnhXMkJFSWNiSDE0QldRd3RsT3d1cXhyd2xrNGs4MjdUc2NTN3kyMm9wd28zWGgrcWFDVVBMXC90czhYQ0dSTzlPajRseGdzXC80WW4wWE9zMVR1N1pYY0pmdHBqZTRjSGphQWVRIn0%3D">here</a></p>
<hr style="text-align: justify; " />
<p style="text-align: justify; ">The inaugural edition of the <a href="http://carnegieindia.org/2016/12/07/global-technology-summit-2016-event-5407">Global Technology Summit</a> convened leading scholars, experts, and officials from more than ten countries for wide-ranging discussions on policy frameworks for technological innovation.</p>
<p style="text-align: justify; ">Building on its success, leading innovators, researchers, and entrepreneurs in cutting-edge technologies from around the world will engage with regulators, policy experts, and civil society actors this December in Bangalore.</p>
<p style="text-align: justify; ">The Summit will focus on new directions in technology policy, such as tech-diplomacy, data protection, and building an innovation ecosystem, as well as fields like digital finance, e-mobility, robotics, and smart cities, where massive technological transformation is likely in the coming years.</p>
<p><a class="external-link" href="http://cis-india.org/internet-governance/files/global-technology-summit-2017-agenda"><b>Agenda here</b></a></p>
<h3>Panel Description</h3>
<p style="text-align: justify; ">Navigating Big Data Challenges: Access to data, and capabilities to analyze the same, redefine the business moat for corporations and governance opportunities for governments. Data dictates product and policy success. It also raises complex challenges. With ever increasing hacks and vulnerabilities, data security continues to confound us. Data-driven businesses and governments also question core assumptions of privacy and individual reputation. Machine learning and deep learning, facilitated by data crunching algorithms, can either be coded to discriminate or learn from human data sets and imbibe the very same prejudices. This panel will deliberate upon these varied challenges, and explore possible policy frameworks to address them.</p>
<p style="text-align: justify; ">The panelists are:</p>
<ul>
<li>Ann Cavoukian</li>
<li>Rahul Matthan</li>
<li>Vishnu Shankar</li>
<li>Rob Sherman</li>
<li>Sunil Abraham</li>
</ul>
<p style="text-align: justify; ">Chaired by B.N. Srikrishna, former judge, Supreme Court of India</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/global-technology-summit-2017'>http://editors.cis-india.org/internet-governance/news/global-technology-summit-2017</a>
</p>
No publisherAdminInternet GovernanceBig Data2017-12-05T13:47:57ZNews Item