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To be Counted When They Count You: Words of Caution for the Gender Data Revolution
http://editors.cis-india.org/raw/to-be-counted-when-they-count-you-words-of-caution-for-the-gender-data-revolution
<b>In 2015, after the announcement of the SDGs or Sustainable Development Goals, a new global developmental framework through the year 2030, the United Nations described data as the “lifeblood of decision-making and the raw material for accountability” for the purpose of realizing these developmental goals. This curious yet key link between these new developmental goals and the use of quantitative data for agenda setting invited a flurry of big data-led initiatives such as but not limited to Data2X, that sought to further strengthen and solidify the relationship between ‘Big Development’ and ‘Big Data.’</b>
<p style="text-align: justify; ">One of those SDG goals (Goal 5) prioritizes gender equality and empowerment of women and girls not only as a standalone goal but also as a crucial factor to realizing the other goals. In response, several academic and non-profit initiatives have begun to interpret and conduct data-led gendered development or the “gender data revolution”. As with other data discourses, the gender-data discourse is also one of ‘speed’, charging ahead using a variety of quantitative and visualization approaches to reveal and eventually solve gendered problems of development.</p>
<p style="text-align: justify; ">These interventions also invite some classical critical questions: who is setting the agenda for the gender data revolution and who are its imagined subjects? How are questions of participation and asymmetries of power in developmental research being addressed? How does the gender data revolution address the situatedness as well as incompleteness of data records in the Global South (where most sites of intervention are)? Speaking specifically to the theme of this special issue (‘cross-cultural feminist technologies’), this paper demonstrates how the welfarist discourse of data-led gender development is, in fact, assembled through the overwhelming enumeration of female-identifying bodies in the Global South.</p>
<p style="text-align: justify; ">The paper offers critical historical insights from the fields of international development, anthropology, and postcolonial history to caution against both, the possible harms of gender disaggregated datafication as well as the consequences of non-participatory datafication of women, the subjects of the gender data revolution.</p>
<p style="text-align: justify; ">Read the full paper <strong><a href="http://editors.cis-india.org/raw/to-be-counted-when-they-count-you.pdf" class="internal-link">here</a></strong>.</p>
<p style="text-align: justify; ">This study was undertaken as part of the Big Data for Development network supported by the International Development Research Centre, Canada, and is shared under Creative Commons Attribution 4.0 International license.</p>
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<p style="text-align: justify; "><span class="discreet">The views and opinions expressed on this page are those of their individual authors. Unless the opposite is explicitly stated, or unless the opposite may be reasonably inferred, CIS does not subscribe to these views and opinions which belong to their individual authors. CIS does not accept any responsibility, legal or otherwise, for the views and opinions of these individual authors. For an official statement from CIS on a particular issue, please contact us directly.</span></p>
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For more details visit <a href='http://editors.cis-india.org/raw/to-be-counted-when-they-count-you-words-of-caution-for-the-gender-data-revolution'>http://editors.cis-india.org/raw/to-be-counted-when-they-count-you-words-of-caution-for-the-gender-data-revolution</a>
</p>
No publishernoopurRAW PublicationsBig DataResearchers at WorkBD4DRAW ResearchBig Data for Development2022-02-01T01:06:08ZBlog EntryEthics and Human Rights Guidelines for Big Data for Development Research
http://editors.cis-india.org/raw/bd4d-ethics-human-rights-guidelines
<b>This is a four-part review of guideline documents for ethics and human rights in big data for development research. This research was produced as part of the Big Data for Development network supported by International Development Research Centre, Canada</b>
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<h4>Part #1 - Review of Principles of Ethics in Biomedical Science: <a href="http://editors.cis-india.org/raw/bd4d-guideline-documents/biomedicalscience" class="internal-link" title="CIS_BD4D_Guideline01_MS+AS_BiomedicalScience PDF">Download</a> (PDF)</h4>
<h4>Part #2 - Review of Principles of Ethics in Computer Science: <a href="http://editors.cis-india.org/raw/bd4d-guideline-documents/computerscience" class="internal-link" title="CIS_BD4D_Guideline02_RS+AS_ComputerScience PDF">Download</a> (PDF)</h4>
<h4>Part #3 - Summary of Review of Codes of Ethics for Big Data and AI: <a href="http://editors.cis-india.org/raw/bd4d-guideline-documents/AIEthicsReview" class="internal-link" title="CIS_BD4D_Guideline03_AS+PT_BigDataAIEthicsReview_SummaryNotes PDF">Download</a> (PDF)</h4>
<h4>Part #4 - Extended Review of Codes of Ethics for Big Data and AI: <a href="http://editors.cis-india.org/raw/bd4d-guideline-documents/ExtendedNotes" class="internal-link" title="CIS_BD4D_Guideline04_PT+PB_BigDataAIEthicsReview_ExtendedNotes PDF">Download</a> (PDF)</h4>
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<p>The rapid expansion in the volume, velocity, and variety of data available, together with the development of innovative forms of statistical analytics, is generally referred to as “big data”; though there is no single agreed upon definition of the term. Big data promises to provide new insights and solutions across a wide range of sectors. Despite enormous optimism about the scope and variety of big data’s potential applications, many remain concerned about its widespread adoption, with some scholars suggesting it could generate as many harms as benefits. The predecessor disciplines of data science such as computer sciences, applied mathematics, and statistics have traditionally managed to stay out of the scope of ethical frameworks, based on the assumption that they do not involve humans as subject of their research. While critical study into big data is still in its infancy, there is a growing belief that there are significant discontinuities between the rapid growth in big data and the ethical framework that exists to govern its use. In this set of documents, we look at them in detail.</p>
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For more details visit <a href='http://editors.cis-india.org/raw/bd4d-ethics-human-rights-guidelines'>http://editors.cis-india.org/raw/bd4d-ethics-human-rights-guidelines</a>
</p>
No publisherAmber Sinha, Manjri Singh, Rajashri Seal, Pranav Bhaskar Tiwari, Pranav M BidareResearchers at WorkBD4DRAW ResearchBig Data for DevelopmentArtificial Intelligence2020-05-20T07:56:48ZBlog EntryIs India's Digital Health System Foolproof?
http://editors.cis-india.org/raw/is-indias-digital-health-system-foolproof
<b>This contribution by Aayush Rathi builds on "Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?" (by Aayush Rathi and Ambika Tandon, EPW Engage, Vol. 54, Issue No. 6, 09 Feb, 2019) and seeks to understand the role that state-run reproductive health portals such as the Mother and Child Tracking System (MCTS) and the Reproductive and Child Health will play going forward. The article critically outlines the overall digitised health information ecosystem being envisioned by the Indian state.</b>
<p> </p>
<h4>This article was first published in <a href="https://www.epw.in/engage/article/indias-digital-health-paradigm-foolproof" target="_blank">EPW Engage, Vol. 54, Issue No. 47</a>, on November 30, 2019</h4>
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<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>
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<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>
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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>
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No publisheraayushEHRBig DataBig Data for DevelopmentResearchBD4DHealthcareResearchers at Work2019-12-30T17:58:00ZBlog EntryThe Mother and Child Tracking System - understanding data trail in the Indian healthcare systems
http://editors.cis-india.org/internet-governance/blog/privacy-international-ambika-tandon-october-17-2019-mother-and-child-tracking-system-understanding-data-trail-indian-healthcare
<b>Reproductive health programmes in India have been digitising extensive data about pregnant women for over a decade, as part of multiple health information systems. These can be seen as precursors to current conceptions of big data systems within health informatics. In this article, published by Privacy International, Ambika Tandon presents some findings from a recently concluded case study of the MCTS as an example of public data-driven initiatives in reproductive health in India. </b>
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<h4>This article was first published by <a href="https://privacyinternational.org/news-analysis/3262/mother-and-child-tracking-system-understanding-data-trail-indian-healthcare" target="_blank">Privacy International</a>, on October 17, 2019</h4>
<h4>Case study of MCTS: <a href="https://cis-india.org/raw/big-data-reproductive-health-india-mcts" target="_blank">Read</a></h4>
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<p>On October 17th 2019, the UN Special Rapporteur (UNSR) on Extreme Poverty and Human Rights, Philip Alston, released his thematic report on digital technology, social protection and human rights. Understanding the impact of technology on the provision of social protection – and, by extent, its impact on people in vulnerable situations – has been part of the work the Centre for Internet and Society (CIS) and Privacy International (PI) have been doing.</p>
<p>Earlier this year, <a href="https://privacyinternational.org/advocacy/2996/privacy-internationals-submission-digital-technology-social-protection-and-human" target="_blank">PI responded</a> to the UNSR's consultation on this topic. We highlighted what we perceived as some of the most pressing issues we had observed around the world when it comes to the use of technology for the delivery of social protection and its impact on the right to privacy and dignity of benefit claimants.</p>
<p>Among them, automation and the increasing reliance on AI is a topic of particular concern - countries including Australia, India, the UK and the US have already started to adopt these technologies in digital welfare programmes. This adoption raises significant concerns about a quickly approaching future, in which computers decide whether or not we get access to the services that allow us to survive. There's an even more pressing problem. More than a few stories have emerged revealing the extent of the bias in many AI systems, biases that create serious issues for people in vulnerable situations, who are already exposed to discrimination, and made worse by increasing reliance on automation.</p>
<p>Beyond the issue of AI, we think it is important to look at welfare and automation with a wider lens. In order for an AI to function it needs to be trained on a dataset, so that it can understand what it is looking for. That requires the collection large quantities of data. That data would then be used to train and AI to recognise what fraudulent use of public benefits would look like. That means we need to think about every data point being collected as one that, in the long run, will likely be used for automation purposes.</p>
<p>These systems incentivise the mass collection of people's data, across a huge range of government services, from welfare to health - where women and gender-diverse people are uniquely impacted. CIS have been looking specifically at reproductive health programmes in India, work which offers a unique insight into the ways in which mass data collection in systems like these can enable abuse.</p>
<p>Reproductive health programmes in India have been digitising extensive data about pregnant women for over a decade, as part of multiple health information systems. These can be seen as precursors to current conceptions of big data systems within health informatics. India’s health programme instituted such an information system in 2009, the Mother and Child Tracking System (MCTS), which is aimed at collecting data on maternal and child health. The Centre for Internet and Society, India, <a href="https://cis-india.org/raw/big-data-reproductive-health-india-mcts" target="_blank">undertook a case study of the MCTS</a> as an example of public data-driven initiatives in reproductive health. The case study was supported by the <a href="http://bd4d.net/" target="_blank">Big Data for Development network</a> supported by the International Development Research Centre, Canada. The objective of the case study was to focus on the data flows and architecture of the system, and identify areas of concern as newer systems of health informatics are introduced on top of existing ones. The case study is also relevant from the perspective of Sustainable Development Goals, which aim to rectify the tendency of global development initiatives to ignore national HIS and create purpose-specific monitoring systems.</p>
<p>After being launched in 2011, 120 million (12 crore) pregnant women and 111 million (11 crore) children have been registered on the MCTS as of 2018. The central database collects data on each visit of the woman from conception to 42 days postpartum, including details of direct benefit transfer of maternity benefit schemes. While data-driven monitoring is a critical exercise to improve health care provision, publicly available documents on the MCTS reflect the complete absence of robust data protection measures. The risk associated with data leaks are amplified due to the stigma associated with abortion, especially for unmarried women or survivors of rape.</p>
<p>The historical landscape of reproductive healthcare provision and family planning in India has been dominated by a target-based approach. Geared at population control, this approach sought to maximise family planning targets without protecting decisional autonomy and bodily privacy for women. At the policy level, this approach was shifted in favour of a rights-based approach to family planning in 1994. However, targets continue to be set for women’s sterilisation on the ground. Surveillance practices in reproductive healthcare are then used to monitor under-performing regions and meet sterilisation targets for women, this continues to be the primary mode of contraception offered by public family planning initiatives.</p>
<p>More recently, this database - among others collecting data about reproductive health - is adding biometric information through linkage with the Aadhaar infrastructure. This data adds to the sensitive information being collected and stored without adhering to any publicly available data protection practices. Biometric linkage is aimed to fulfill multiple functions - primarily authentication of welfare beneficiaries of the national maternal benefits scheme. Making Aadhaar details mandatory could directly contribute to the denial of service to legitimate patients and beneficiaries - as has already been seen in some cases.</p>
<p>The added layer of biometric surveillance also has the potential to enable other forms of abuse of privacy for pregnant women. In 2016, the union minister for Women and Child Development under the previous government suggested the use of strict biometric-based monitoring to discourage gender-biased sex selection. Activists critiqued the policy for its paternalistic approach to reduce the rampant practice of gender-biased sex selection, rather than addressing the root causes of gender inequality in the country.</p>
<p>There is an urgent need to rethink the objectives and practices of data collection in public reproductive health provision in India. Rather than continued focus on meeting high-level targets, monitoring systems should enable local usage and protect the decisional autonomy of patients. In addition, the data protection legislation in India - expected to be tabled in the next session in parliament - should place free and informed consent, and informational privacy at the centre of data-driven practices in reproductive health provision.</p>
<p>This is why the systematic mass collection of data in health services is all the more worrying. When the collection of our data becomes a condition for accessing health services, it is not only a threat to our right to health that should not be conditional on data sharing but also it raises questions as to how this data will be used in the age of automation.</p>
<p>This is why understanding what data is collected and how it is collected in the context of health and social protection programmes is so important.</p>
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For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/privacy-international-ambika-tandon-october-17-2019-mother-and-child-tracking-system-understanding-data-trail-indian-healthcare'>http://editors.cis-india.org/internet-governance/blog/privacy-international-ambika-tandon-october-17-2019-mother-and-child-tracking-system-understanding-data-trail-indian-healthcare</a>
</p>
No publisherambikaBig DataData SystemsPrivacyResearchers at WorkInternet GovernanceResearchBD4DHealthcareBig Data for Development2019-12-30T17:18:05ZBlog EntryBig Data and Reproductive Health in India: A Case Study of the Mother and Child Tracking System
http://editors.cis-india.org/raw/big-data-reproductive-health-india-mcts
<b>In this case study undertaken as part of the Big Data for Development (BD4D) network, Ambika Tandon evaluates the Mother and Child Tracking System (MCTS) as data-driven initiative in reproductive health at the national level in India. The study also assesses the potential of MCTS to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data.</b>
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<h4>Case study: <a href="https://github.com/cis-india/website/raw/master/bd4d/CIS_CaseStudy_AT_BigDataReproductiveHealthMCTS.pdf" target="_blank">Download</a> (PDF)</h4>
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<h3>Introduction</h3>
<p>The reproductive health information ecosystem in India comprises of a range of different databases across state and national levels. These collect data through a combination of manual and digital tools. Two national-level databases have been launched by the Ministry of Health and Family Welfare - the Health Management Information System (HMIS) in 2008, and the MCTS in 2009. 4 The MCTS focuses on collecting data on maternal and child health. It was instituted due to reported gaps in the HMIS, which records monthly data across health programmes including reproductive health. There are several other state-level initiatives on reproductive health data that have either been subsumed into, or run in
parallel with, the MCTS.</p>
<p>With this case study, we aim to evaluate the MCTS as data-driven initiative in reproductive health at the national level. It will also assess its potential to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data. The methodology for the case study involved a desk-based review of existing literature on the use of health information systems globally, as well as analysis of government reports, journal articles, media coverage, policy documents, and other material on the MCTS.</p>
<p>The first section of this report details the theoretical framing of the case study, drawing on the feminist critique of reproductive data systems. The second section maps the current landscape of reproductive health data produced by the state in India, with a focus on data flows, and barriers to data collection and analysis at the local and national level. The case of abortion data is used to further the argument of flawed data collection systems at the
national level. Section three briefly discusses the state’s imagination of reproductive health policy and the role of data systems through a discussion on the National Health Policy, 2017 and the National Health Stack, 2018. Finally, we make some policy recommendations and identify directions for future research, taking into account the ongoing shift towards big data globally to democratise reproductive healthcare.</p>
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For more details visit <a href='http://editors.cis-india.org/raw/big-data-reproductive-health-india-mcts'>http://editors.cis-india.org/raw/big-data-reproductive-health-india-mcts</a>
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No publisherambikaBig DataData SystemsResearchers at WorkReproductive and Child HealthResearchFeaturedPublicationsBD4DHealthcareBig Data for Development2019-12-06T04:57:55ZBlog EntryYou auto-complete me: romancing the bot
http://editors.cis-india.org/raw/maya-indira-ganesh-you-auto-complete-me-romancing-the-bot
<b>This is an excerpt from an essay by Maya Indira Ganesh, written for and published as part of the Bodies of Evidence collection of Deep Dives. The Bodies of Evidence collection, edited by Bishakha Datta and Richa Kaul Padte, is a collaboration between Point of View and the Centre for Internet and Society, undertaken as part of the Big Data for Development Network supported by International Development Research Centre, Canada. </b>
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<h4>Please read the full essay on Deep Dives: <a href="https://deepdives.in/you-auto-complete-me-romancing-the-bot-f2f16613fec8" target="_blank">You auto-complete me: romancing the bot</a></h4>
<h4>Maya Indira Ganesh: <a href="https://bodyofwork.in/" target="_blank">Website</a> and <a href="https://twitter.com/mayameme" target="_blank">Twitter</a></h4>
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<p>I feel like Kismet the Robot.</p>
<p>Kismet is a flappy-eared animatronic head with oversized eyeballs and bushy eyebrows. Connected to cameras and sensors, it exhibits the six primary human emotions identified by psychologist Paul Ekman: happiness, sadness, disgust, surprise, anger, and fear.</p>
<p>Scholar Katherine Hayles says that Kismet was built as an ‘ecological whole’ to respond to both humans and the environment. ‘The community,’ she writes, ‘understood as the robot plus its human interlocutors, is greater than the sum of its parts, because the robot’s design and programming have been created to optimise interactions with humans.’</p>
<p>In other words, Kismet may have ‘social intelligence’.</p>
<p>Kismet’s creator Cynthia Breazal explains this through a telling example. If someone comes too close to it, Kismet retracts its head as if to suggest that its personal space is being violated, or that it is shy. In reality, it is trying to adjust its camera so that it can properly see whatever is in front of it. But it is the human interacting with Kismet who interprets this retraction as the robot requiring its own space by moving back. Breazal says, ‘Human interpretation and response make the robot’s actions more meaningful than they otherwise would be.’</p>
<p>In other words, humans interpret Kismet’s social intelligence as ‘emotional intelligence’...</p>
<p>Kismet was built at the start of a new field called affective computing, which is now branded as ‘emotion AI’. Affective computing is about analysing human facial expressions, gait and stance into a map of emotional states. Here is what Affectiva, one of the companies developing this technology, says about how it works:</p>
<p>‘Humans use a lot of non-verbal cues, such as facial expressions, gesture, body language and tone of voice, to communicate their emotions. Our vision is to develop Emotion AI that can detect emotion just the way humans do. Our technology first identifies a human face in real time or in an image or video. Computer vision algorithms then identify key landmarks on the face…[and] deep learning algorithms analyse pixels in those regions to classify facial expressions. Combinations of these facial expressions are then mapped to emotions.’</p>
<p>But there is also a more sinister aspect to this digitised love-fest. Our faces, voices, and selfies are being used to collect data to train future bots to be more realistic. There is an entire industry of Emotion AI that harvests human emotional data to build technologies that we are supposed to enjoy because they appear more human. But it often comes down to a question of social control, because the same emotional data is used to track, monitor and regulate our own emotions and behaviours...</p>
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For more details visit <a href='http://editors.cis-india.org/raw/maya-indira-ganesh-you-auto-complete-me-romancing-the-bot'>http://editors.cis-india.org/raw/maya-indira-ganesh-you-auto-complete-me-romancing-the-bot</a>
</p>
No publishersumandroBodies of EvidenceResearchers at WorkResearchPublicationsBD4DBotsBig Data for Development2019-12-06T05:00:19ZBlog EntryData bleeding everywhere: a story of period trackers
http://editors.cis-india.org/raw/sadaf-khan-data-bleeding-everywhere-a-story-of-period-trackers
<b>This is an excerpt from an essay by Sadaf Khan, written for and published as part of the Bodies of Evidence collection of Deep Dives. The Bodies of Evidence collection, edited by Bishakha Datta and Richa Kaul Padte, is a collaboration between Point of View and the Centre for Internet and Society, undertaken as part of the Big Data for Development Network supported by International Development Research Centre, Canada.</b>
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<h4>Please read the full essay on Deep Dives: <a href="https://deepdives.in/data-bleeding-everywhere-a-story-of-period-trackers-8766dc6a1e00" target="_blank">Data bleeding everywhere: a story of period trackers</a></h4>
<h4>Sadaf Khan: <a href="http://mediamatters.pk/the-team/" target="_blank">Media Matters for Democracy</a> and <a href="https://twitter.com/nuqsh" target="_blank">Twitter</a></h4>
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<p>...By now there are a number of questions buzzing around my head, most of them unasked. Are users comfortable with so much of their data being collected? Are there really algorithms that string together all this data into medically-relevant trends? How reliable can these trends be when usage is erratic? Are period tracking apps pioneering, fundamental elements of a future where medical aid is digital and reliable data is inevitably linked to the provision of medical services? And if so, are privacy and health soon to become conflicting rights?</p>
<p>I also want to find out how users understand data collection and privacy before giving apps consent to utilize their data and information as they will. Hareem says she gives apps informed consent. ‘If my data becomes a part of the statistics aiding medical research, why not? There is no harm in it. I am getting a good service, and if my data helps create a better understanding as a part of a larger statistical pool, they are welcome to use it.’</p>
<p>But is she really sure that this information will be used only as anonymised data for medical research? ‘Look at the kind of information that is being collected,’ she answers. ‘Dates, mood, consistency of mucus, basal temperature. What kind of use does one have for this data?’</p>
<p>Naila, in turn, says: ‘Honestly, I have never really thought about what happens to the data the application collects. Obviously I enter detailed information about my cycle and my moods and my sex life. But a), my account is under a fake name and b), even if it wasn’t, who would have any use for stuff like when my period starts and ends and what my mood or digestive system is like at any given moment?’</p>
<p>In fact, this sentiment is shared among all the women interviewed for this piece — what use would anyone have for this data?</p>
<p>As users, we often imagine our own data as anonymised within a huge dataset. But as users, we don’t have enough information about how our data is being used — or will be used in future. The open and at times vague language of a platform’s terms and conditions allows menstrual apps to use data in ways that I may not know of. Some apps continue to hold customer data even after an account is deleted. Even though I may technically ‘agree’ to the terms and conditions, is this fully informed consent?</p>
<p>One of the big concerns around this kind of medical information being collected is the potential for collaborations with big pharmaceuticals and other health service providers. With apps sitting on a goldmine of users’ fertility and health information, health service providers might mine their data for potential consumers and reach out directly to them. While this is like any targeted marketing campaign, the fact that the advertiser is likely to be offering medical services to women suffering from infertility and are at their most vulnerable, raises totally different ethical concerns.</p>
<p>And these apps and their businesses might grow in directions that users haven’t taken into consideration. Take Ovia’s health feature for companies to buy premium services for their employees. While the gesture is packaged as a goodwill one, it also means that an employer has access to extremely private and intimate medical information about their women employees. And while the data set is anonymised, it is still possible to figure out the identity of users based on specific information. For example, how many women in any company are pregnant at any given time?...</p>
<p>Pregnant a year after my miscarriage, I initially downloaded multiple apps in a bid to find a good fit. I don’t know which one of these was in communication with Facebook. But almost immediately, my Facebook timeline started becoming littered with ads for baby stuff — clothes, shoes bibs, prams, cribs, ointments for stretch marks, maternity wear, the works.</p>
<p>It makes me think of those old school clockwork-style videos. You drop a ball and off it goes: making dominos fall, knocking over pots and pans, setting in motion absurd, synchronized mechanisms. Similarly, I drop my data and watch it hurtle into my life, on to other platforms, off to vendors. Maybe to stalkers? To employers? Who knows.</p>
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For more details visit <a href='http://editors.cis-india.org/raw/sadaf-khan-data-bleeding-everywhere-a-story-of-period-trackers'>http://editors.cis-india.org/raw/sadaf-khan-data-bleeding-everywhere-a-story-of-period-trackers</a>
</p>
No publishersumandroBodies of EvidenceResearchers at WorkResearchFeaturedPublicationsBD4DBig Data for Development2019-12-06T05:03:09ZBlog EntryCan data ever know who we really are?
http://editors.cis-india.org/raw/zara-rahman-can-data-ever-know-who-we-really-are
<b>This is an excerpt from an essay by Zara Rahman, written for and published as part of the Bodies of Evidence collection of Deep Dives. The Bodies of Evidence collection, edited by Bishakha Datta and Richa Kaul Padte, is a collaboration between Point of View and the Centre for Internet and Society, undertaken as part of the Big Data for Development Network supported by International Development Research Centre, Canada.</b>
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<h4>Please read the full essay on Deep Dives: <a href="https://deepdives.in/can-data-ever-know-who-we-really-are-a0dbfb5a87a0" target="_blank">Can data ever know who we really are?</a></h4>
<h4>Zara Rahman: <a href="https://www.theengineroom.org/people/zara-rahman/" target="_blank">The Engine Room</a>, <a href="https://zararah.net/" target="_blank">Website</a>, and <a href="https://twitter.com/zararah" target="_blank">Twitter</a></h4>
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<blockquote>If I didn’t define myself for myself, I would be crunched into other people’s fantasies for me and eaten alive.<br /><em>– <a href="https://www.blackpast.org/african-american-history/1982-audre-lorde-learning-60s/" target="_blank">Audre Lorde</a></em></blockquote>
<p>The proliferation of digital data and the technologies that allow us to gather that data can be used in another way too — to allow us to define for ourselves who we are, and what we are.</p>
<p>Amidst a growing political climate of fear, mistrust and competition for resources, activists and advocates working in areas that are stigmatised within their societies often need data to ‘prove’ that what they are working on matters. One way of doing this is by gathering data through crowdsourcing. Crowdsourced data isn’t ‘representative’, as statisticians say, but gathering data through unofficial means can be a valuable asset for advocates. For example, <a href="http://readytoreport.in/" target="_blank">data collating the experiences of women</a> who have reported incidents of sexual violence to the police in India, can then be used to advocate for better police responses, and to inform women of their rights. Deservedly or not, quantifiable data takes precedence over personal histories and lived experience in getting the much-desired currency of attention.</p>
<p>And used right, quantifiable data — whether it’s crowdsourced or not — can also be a powerful tool for advocates. Now, we can use quantifiable data to prove beyond a question of a doubt that disabled people, queer people, people from lower castes, face intersecting discrimination, prejudice, and systemic injustices in their lives. It’s an unnecessary repetition in a way, because anybody from those communities could have told reams upon reams of stories about discrimination — all without any need for counting.</p>
<p>Regardless, to play within this increasingly digitised system, we need to repeat what we’ve been saying in a new, digitally-legible way. And to do that, we need to collect data from people who have often only ever been de-humanised as data subjects.</p>
<p>Artist and educator Mimi Onuoha writes about <a href="https://points.datasociety.net/the-point-of-collection-8ee44ad7c2fa#.y0xtfxi2p" target="_blank">the challenges that arise while collecting such data</a>, from acknowledging the humans behind that collection to understanding that missing data points might tell just as much of a story as the data that has been collected. She outlines how digital data means that we have to (intentionally or not) make certain choices about what we value. And the collection of this data means making human choices solid, and often (though not always) making these choices illegible to others.</p>
<p>We speak of black boxes when it comes to <a href="https://www.propublica.org/article/breaking-the-black-box-what-facebook-knows-about-you" target="_blank">the mystery choices that algorithms make</a>, but the same could be said of the many human decisions that are made in categorising data too, whether that be choosing to limit the gender drop-down field to just ‘male/female’ as with Fitbits, or a variety of apps incorrectly assuming that all people who menstruate <a href="https://medium.com/@maggied/i-tried-tracking-my-period-and-it-was-even-worse-than-i-could-have-imagined-bb46f869f45" target="_blank">also want to know about their ‘fertile window’</a>. In large systems with many humans and machines at work, we have no way of interrogating why a category was merged or not, of understanding why certain anomalies were ignored rather than incorporated, or of questioning why certain assumptions were made.</p>
<p>The only thing we can do is to acknowledge these limitations, and try to use those very systems to our advantage, building our own alternatives or workarounds, collecting our own data, and using the data that is out there to tell the stories that matter to us.</p>
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For more details visit <a href='http://editors.cis-india.org/raw/zara-rahman-can-data-ever-know-who-we-really-are'>http://editors.cis-india.org/raw/zara-rahman-can-data-ever-know-who-we-really-are</a>
</p>
No publishersumandroBodies of EvidenceBig DataData SystemsResearchers at WorkResearchPublicationsBD4DBig Data for Development2019-12-06T05:02:53ZBlog EntryData Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?
http://editors.cis-india.org/internet-governance/blog/data-infrastructures-inequities-reproductive-health-surveillance-india
<b>In order to bring out certain conceptual and procedural problems with health monitoring in the Indian context, this article by Aayush Rathi and Ambika Tandon posits health monitoring as surveillance and not merely as a “data problem.” Casting a critical feminist lens, the historicity of surveillance practices unveils the gendered power differentials wedded into taken-for-granted “benign” monitoring processes. The unpacking of the Mother and Child Tracking System and the National Health Stack reveals the neo-liberal aspirations of the Indian state. </b>
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<p><em>The article was first published by <a href="https://www.epw.in/engage/article/data-infrastructures-inequities-why-does-reproductive-health-surveillance-india-need-urgent-attention" target="_blank">EPW Engage, Vol. 54, Issue No. 6</a>, on 9 February 2019.</em></p>
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<h3><strong>Framing Reproductive Health as a Surveillance Question</strong></h3>
<p>The approach of the postcolonial Indian state to healthcare has been Malthusian, with the prioritisation of family planning and birth control (Hodges 2004). Supported by the notion of socio-economic development arising out of a “modernisation” paradigm, the target-based approach to achieving reduced fertility rates has shaped India’s reproductive and child health (RCH) programme (Simon-Kumar 2006).</p>
<p>This is also the context in which India’s abortion law, the Medical Termination of Pregnancy (MTP) Act, was framed in 1971, placing the decisional privacy of women seeking abortions in the hands of registered medical practitioners. The framing of the MTP act invisibilises females seeking abortions for non-medical reasons within the legal framework. The exclusionary provisions only exacerbated existing gaps in health provisioning, as access to safe and legal abortions had already been curtailed by severe geographic inequalities in funding, infrastructure, and human resources. The state has concomitantly been unable to meet contraceptive needs of married couples or reduce maternal and infant mortality rates in large parts of the country, mediating access along the lines of class, social status, education, and age (Sanneving et al 2013).</p>
<p>While the official narrative around the RCH programme transitioned to focus on universal access to healthcare in the 1990s, the target-based approach continues to shape the reality on the ground. The provision of reproductive healthcare has been deeply unequal and, in some cases, in hospitals. These targets have been known to be met through the practice of forced, and often unsafe, sterilisation, in conditions of absence of adequate provisions or trained professionals, pre-sterilisation counselling, or alternative forms of contraception (Sama and PLD 2018). Further, patients have regularly been provided cash incentives, foreclosing the notion of free consent, especially given that the target population of these camps has been women from marginalised economic classes in rural India.</p>
<p>Placing surveillance studies within a feminist praxis allows us to frame the reproductive health landscape as more than just an ill-conceived, benign monitoring structure. The critical lens becomes useful for highlighting that taken-for-granted structures of monitoring are wedded with power differentials: genetic screening in fertility clinics, identification documents such as birth certificates, and full-body screeners are just some of the manifestations of this (Adrejevic 2015). Emerging conversations around feminist surveillance studies highlight that these data systems are neither benign nor free of gendered implications (Andrejevic 2015). In continual remaking of the social, corporeal body as a data actor in society, such practices render some bodies normative and obfuscate others, based on categorisations put in place by the surveiller.</p>
<p>In fact, the history of surveillance can be traced back to the colonial state where it took the form of systematic sexual and gendered violence enacted upon indigenous populations in order to render them compliant (Rifkin 2011; Morgensen 2011). Surveillance, then, manifests as a “scientific” rationalisation of complex social hieroglyphs (such as reproductive health) into formats enabling administrative interventions by the modern state. Lyon (2001) has also emphasised how the body emerged as the site of surveillance in order for the disciplining of the “irrational, sensual body”—essential to the functioning of the modern nation-state—to effectively happen.</p>
<h3><strong>Questioning the Information and Communications Technology for Development (ICT4D) and Big Data for Development (BD4D) Rhetoric</strong></h3>
<p>Information and Communications Technology (ICT) and data-driven approaches to the development of a robust health information system, and by extension, welfare, have been offered as solutions to these inequities and exclusions in access to maternal and reproductive healthcare in the country.</p>
<p>The move towards data-driven development in the country commenced with the introduction of the Health Management Information System in Andhra Pradesh in 2008, and the Mother and Child Tracking System (MCTS) nationally in 2011. These are reproductive health information systems (HIS) that collect granular data about each pregnancy from the antenatal to the post-natal period, at the level of each sub-centre as well as primary and community health centre. The introduction of HIS comprised cross-sectoral digitisation measures that were a part of the larger national push towards e-governance; along with health, thirty other distinct areas of governance, from land records to banking to employment, were identified for this move towards the digitalised provisioning of services (MeitY 2015).</p>
<p>The HIS have been seen as playing a critical role in the ecosystem of health service provision globally. HIS-based interventions in reproductive health programming have been envisioned as a means of: (i) improving access to services in the context of a healthcare system ridden with inequalities; (ii) improving the quality of services provided, and (iii) producing better quality data to facilitate the objectives of India’s RCH programme, including family planning and population control. Accordingly, starting 2018, the MCTS is being replaced by the RCH portal in a phased manner. The RCH portal, in areas where the ANMOL (ANM Online) application has been introduced, captures data real-time through tablets provided to health workers (MoHFW 2015).</p>
<p>A proposal to mandatorily link the Aadhaar with data on pregnancies and abortions through the MCTS/RCH has been made by the union minister for Women and Child Development as a deterrent to gender-biased sex selection (Tembhekar 2016). The proposal stems from the prohibition of gender-biased sex selection provided under the Pre-Conception and Pre-Natal Diagnostics Techniques (PCPNDT) Act, 1994. The approach taken so far under the PCPNDT Act, 2014 has been to regulate the use of technologies involved in sex determination. However, the steady decline in the national sex ratio since the passage of the PCPNDT Act provides a clear indication that the regulation of such technology has been largely ineffective. A national policy linking Aadhaar with abortions would be aimed at discouraging gender-biased sex selection through state surveillance, in direct violation of a female’s right to decisional privacy with regards to their own body.</p>
<p>Linking Aadhaar would also be used as a mechanism to enable direct benefit transfer (DBT) to the beneficiaries of the national maternal benefits scheme. Linking reproductive health services to the Aadhaar ecosystem has been critiqued because it is exclusionary towards women with legitimate claims towards abortions and other reproductive services and benefits, and it heightens the risk of data breaches in a cultural fabric that already stigmatises abortions. The bodies on which this stigma is disproportionately placed, unmarried or disabled females, for instance, experience the harms of visibility through centralised surveillance mechanisms more acutely than others by being penalised for their deviance from cultural expectations. This is in accordance with the theory of "data extremes,” wherein marginalised communities are seen as living on the extremes of data capture, leading to a data regime that either refuses to recognise them as legitimate entities or subjects them to overpolicing in order to discipline deviance (Arora 2016). In both developed and developing contexts, the broader purpose of identity management has largely been to demarcate legitimate and illegitimate actors within a population, either within the framework of security or welfare.</p>
<h3><strong>Potential Harms of the Data Model of Reproductive Health Provisioning</strong></h3>
<p>Informational privacy and decisional privacy are critically shaped by data flows and security within the MCTS/RCH. No standards for data sharing and storage, or anonymisation and encryption of data have been implemented despite role-based authentication (NHSRC and Taurus Glocal 2011). The risks of this architectural design are further amplified in the context of the RCH/ANMOL where data is captured real-time. In the absence of adequate safeguards against data leaks, real-time data capture risks the publicising of reproductive health choices in an already stigmatised environment. This opens up avenues for further dilution of autonomy in making future reproductive health choices.</p>
<p>Several core principles of informational privacy, such as limitations regarding data collection and usage, or informed consent, also need to be reworked within this context.<sup>[1]</sup> For instance, the centrality of the requirement of “free, informed consent” by an individual would need to be replaced by other models, especially in the context of reproductive health of rape survivors who are vulnerable and therefore unable to exercise full agency. The ability to make a free and informed choice, already dismantled in the context of contemporary data regimes, gets further precluded in such contexts. The constraints on privacy in decisions regarding the body are then replicated in the domain of reproductive data collection.</p>
<p>What is uniform across these digitisation initiatives is their treatment of maternal and reproductive health as solely a medical event, framed as a data scarcity problem. In doing so, they tend to amplify the understanding of reproductive health through measurable indicators that ignore social determinants of health. For instance, several studies conducted in the rural Indian context have shown that the degree of women’s autonomy influences the degree of usage of pregnancy care, and that the uptake of pregnancy care was associated with village-level indicators such as economic development, provisioning of basic infrastructure and social cohesion. These contextual factors get overridden in pervasive surveillance systems that treat reproductive healthcare as comprising only of measurable indicators and behaviours, that are dependent on individual behaviour of practitioners and women themselves, rather than structural gaps within the system.</p>
<p>While traditionally associated with state governance, the contemporary surveillance regime is experienced as distinct from its earlier forms due to its reliance on a nexus between surveillance by the state and private institutions and actors, with both legal frameworks and material apparatuses for data collection and sharing (Shepherd 2017). As with historical forms of surveillance, the harms of contemporary data regimes accrue disproportionately among already marginalised and dissenting communities and individuals. Data-driven surveillance has been critiqued for its excesses in multiple contexts globally, including in the domains of predictive policing, health management, and targeted advertising (Mason 2015). In the attempts to achieve these objectives, surveillance systems have been criticised for their reliance on replicating past patterns, reifying proximity to a hetero-patriarchal norm (Haggerty and Ericson 2000). Under data-driven surveillance systems, this proximity informs the preexisting boxes of identity for which algorithmic representations of the individual are formed. The boxes are defined contingent on the distinct objectives of the particular surveillance project, collating disparate pieces of data flows and resulting in the recasting of the singular offline self into various 'data doubles' (Haggerty and Ericson 2000). Refractive, rather than reflective, the data doubles have implications for the physical, embodied life of individual with an increasing number of service provisioning relying on the data doubles (Lyon 2001). Consider, for instance, apps on menstruation, fertility, and health, and wearables such as fitness trackers and pacers, that support corporate agendas around what a woman’s healthy body should look, be or behave like (Lupton 2014). Once viewed through the lens of power relations, the fetishised, apolitical notion of the data “revolution” gives way to what we may better understand as “dataveillance.”</p>
<h3><strong>Towards a Networked State and a Neo-liberal Citizen</strong></h3>
<p>Following in this tradition of ICT being treated as the solution to problems plaguing India’s public health information system, a larger, all-pervasive healthcare ecosystem is now being proposed by the Indian state (NITI Aayog 2018). Termed the National Health Stack, it seeks to create a centralised electronic repository of health records of Indian citizens with the aim of capturing every instance of healthcare service usage. Among other functions, it also envisions a platform for the provisioning of health and wellness-based services that may be dispensed by public or private actors in an attempt to achieve universal health coverage. By allowing private parties to utilise the data collected through pullable open application program interfaces (APIs), it also fits within the larger framework of the National Health Policy 2017 that envisions the private sector playing a significant role in the provision of healthcare in India. It also then fits within the state–private sector nexus that characterises dataveillance. This, in turn, follows broader trends towards market-driven solutions and private financing of health sector reform measures that have already had profound consequences on the political economy of healthcare worldwide (Joe et al 2018).</p>
<p>These initiatives are, in many ways, emblematic of the growing adoption of network governance reform by the Indian state (Newman 2001). This is a stark shift from its traditional posturing as the hegemonic sovereign nation state. This shift entails the delayering from large, hierarchical and unitary government systems to horizontally arranged, more flexible, relatively dispersed systems.<sup>[2]</sup> The former govern through the power of rules and law, while the latter take the shape of self-regulating networks such as public–private contractual arrangements (Snellen 2005). ICTs have been posited as an effective tool in enabling the transition to network governance by enhancing local governance and interactive policymaking enabling the co-production of knowledge (Ferlie et al 2011). The development of these capabilities is also critical to addressing “wicked problems” such as healthcare (Rittel and Webber 1973).<sup>[3]</sup> The application of the techno-deterministic, data-driven model to reproductive healthcare provision, then, resembles a fetishised approach to technological change. The NHSRC describes this as the collection of data without an objective, leading to a disproportional burden on data collection over use (NHSRC and Taurus Glocal 2011).</p>
<p>The blurring of the functions of state and private actors is reflective of the neo-liberal ethic, which produces new practices of governmentality. Within the neo-liberal framework of reproductive healthcare, the citizen is constructed as an individual actor, with agency over and responsibility for their own health and well-being (Maturo et al 2016).</p>
<h3><strong>“Quantified Self” of the Neo-liberal Citizen</strong></h3>
<p>Nowhere can the manifestation of this neo-liberal citizen can be seen as clearly as in the “quantified self” movement. The quantified self movement refers to the emergence of a whole range of apps that enable the user to track bodily functions and record data to achieve wellness and health goals, including menstruation, fertility, pregnancies, and health indicators in the mother and baby. Lupton (2015) labels this as the emergence of the “digitised reproductive citizen,” who is expected to be attentive to her fertility and sexual behaviour to achieve better reproductive health goals. The practice of collecting data around reproductive health is not new to the individual or the state, as has been demonstrated by the discussion above. What is new in this regime of datafication under the self-tracking movement is the monetisation of reproductive health data by private actors, the labour for which is performed by the user. Focusing on embodiment draws attention to different kinds of exploitation engendered by reproductive health apps. Not only is data about the body collected and sold, the unpaid labour for collection is extracted from the user. The reproductive body can then be understood as a cyborg, or a woman-machine hybrid, systematically digitising its bodily functions for profit-making within the capitalist (re)production machine (Fotoloulou 2016). Accordingly, all major reproductive health tracking apps have a business model that relies on selling information about users for direct marketing of products around reproductive health and well-being (Felizi and Varon nd).</p>
<p>As has been pointed out in the case of big data more broadly, reproductive health applications (apps) facilitate the visibility of the female reproductive body in the public domain. Supplying anonymised data sets to medical researchers and universities fills some of the historical gaps in research around the female body and reproductive health. Reproductive and sexual health tracking apps globally provide their users a platform to engage with biomedical information around sexual and reproductive health. Through group chats on the platform, they are also able to engage with experiential knowledge of sexual and reproductive health. This could also help form transnational networks of solidarity around the body and health (Fotopoulou 2016).</p>
<p style="text-align: justify;">This radical potential of network-building around reproductive and sexual health is, however, tempered to a large extent by the reconfiguration of gendered stereotypes through these apps. In a study on reproductive health apps on Google Play Store, Lupton (2014) finds that products targeted towards female users are marketed through the discourse of risk and vulnerability, while those targeted towards male users are framed within that of virility. Apart from reiterating gendered stereotypes around the male and female body, such a discourse assumes that the entire labour of family planning is performed by females. This same is the case with the MCTS/RCH.</p>
<p>Technological interventions such as reproductive health apps as well as HIS are based on the assumption that females have perfect control over decisions regarding their own bodies and reproductive health, despite this being disproved in India. The Guttmacher Institute (2014) has found that 60% of women in India report not having control over decisions regarding their own healthcare. The failure to account for the husband or the family as stakeholder in decision-making around reproductive health has been a historical failure of the family planning programme in India, and is now being replicated in other modalities. This notion of an autonomous citizen who is able to take responsibility of their own reproductive health and well-being does not hold true in the Indian context. It can even be seen as marginalising females who have already been excluded from the reproductive health system, as they are held responsible for their own inability to access healthcare.</p>
<h3><strong>Concluding Remarks</strong></h3>
<p>The interplay that emerges between reproductive health surveillance and data infrastructures is a complex one. It requires the careful positioning of the political nature of data collection and processing as well as its hetero-patriarchal and colonial legacies, within the need for effective utilisation of data for achieving developmental goals. Assessing this discourse through a feminist lens identifies the web of power relations in data regimes. This problematises narratives of technological solutions for welfare provision.</p>
<p>The reproductive healthcare framework in India then offers up a useful case study to assess these concerns. The growing adoption of ICT-based surveillance tools to equalise access to healthcare needs to be understood in the socio-economic, legal, and cultural context where these tools are being implemented. Increased surveillance has historically been associated with causing the structural gendered violence that it is now being offered as a solution to. This is a function of normative standards being constructed for reproductive behaviour that necessarily leave out broader definitions of reproductive health and welfare when viewed through a feminist lens. Within the larger context of health policymaking in India, moves towards privatisation then demonstrate the peculiarity of dataveillance as it functions through an unaccountable and pervasive overlapping of state and private surveillance practises. It remains to be seen how these trends in ICT-driven health policies affect access to reproductive rights and decisional privacy for millions of females in India and other parts of the global South.</p>
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For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/data-infrastructures-inequities-reproductive-health-surveillance-india'>http://editors.cis-india.org/internet-governance/blog/data-infrastructures-inequities-reproductive-health-surveillance-india</a>
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No publisherAayush Rathi and Ambika TandonBig DataData SystemsPrivacyResearchers at WorkInternet GovernanceResearchBD4DHealthcareSurveillanceBig Data for Development2019-12-30T16:44:32ZBlog Entry