Centre for Internet and Society

Ethics and Human Rights Guidelines for Big Data for Development Research

Amber Sinha, Manjri Singh, Rajashri Seal, Pranav Bhaskar Tiwari, Pranav M Bidare — 2020-05-20T07:56:48Z

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

Part #1 - Review of Principles of Ethics in Biomedical Science: Download (PDF)

Part #2 - Review of Principles of Ethics in Computer Science: Download (PDF)

Part #3 - Summary of Review of Codes of Ethics for Big Data and AI: Download (PDF)

Part #4 - Extended Review of Codes of Ethics for Big Data and AI: Download (PDF)

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.

For more details visit https://cis-india.org/raw/bd4d-ethics-human-rights-guidelines

Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?

Aayush Rathi and Ambika Tandon — 2019-12-30T16:44:32Z

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.

The article was first published by EPW Engage, Vol. 54, Issue No. 6, on 9 February 2019.

Framing Reproductive Health as a Surveillance Question

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).

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).

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.

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.

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.

Questioning the Information and Communications Technology for Development (ICT4D) and Big Data for Development (BD4D) Rhetoric

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.

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).

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).

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.

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.

Potential Harms of the Data Model of Reproductive Health Provisioning

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.

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.^[1] 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.

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.

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.”

Towards a Networked State and a Neo-liberal Citizen

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).

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.^[2] 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).^[3] 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).

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).

“Quantified Self” of the Neo-liberal Citizen

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).

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).

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.

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.

Concluding Remarks

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.

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.

For more details visit https://cis-india.org/internet-governance/blog/data-infrastructures-inequities-reproductive-health-surveillance-india

Data bleeding everywhere: a story of period trackers

sumandro — 2019-12-06T05:03:09Z

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.

Please read the full essay on Deep Dives: Data bleeding everywhere: a story of period trackers

Sadaf Khan: Media Matters for Democracy and Twitter

...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?

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.’

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?’

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?’

In fact, this sentiment is shared among all the women interviewed for this piece — what use would anyone have for this data?

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?

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.

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?...

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.

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.

For more details visit https://cis-india.org/raw/sadaf-khan-data-bleeding-everywhere-a-story-of-period-trackers

CFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)

sumandro — 2019-03-16T04:41:52Z

The Centre for Financial Inclusion of ACCION International is organising a panel discussion on "Big Data: Challenge or Opportunity?" as an associated event of the Inclusive Finance India Summit 2016, Hotel Ashok, Delhi, December 05-06. The discussion will be held at 12:30 on Tuesday, December 06. It will be moderated by Amy Jensen Mowl, CFI Fellow at IFMR, and M.S. Sriram, Distinguished Fellow at the Institute for Development of Research in Banking Technology. Sumandro Chattapadhyay will participate as a panelist.

Inclusive Finance India Summit: http://inclusivefinanceindia.org/.

For more details visit https://cis-india.org/internet-governance/news/cfi-accion-panel-discussion-on-big-data-delhi-dec-06

Can data ever know who we really are?

sumandro — 2019-12-06T05:02:53Z

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.

Please read the full essay on Deep Dives: Can data ever know who we really are?

Zara Rahman: The Engine Room, Website, and Twitter

If I didn’t define myself for myself, I would be crunched into other people’s fantasies for me and eaten alive.
– Audre Lorde

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.

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, data collating the experiences of women 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.

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.

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.

Artist and educator Mimi Onuoha writes about the challenges that arise while collecting such data, 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.

We speak of black boxes when it comes to the mystery choices that algorithms make, 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 also want to know about their ‘fertile window’. 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.

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.

For more details visit https://cis-india.org/raw/zara-rahman-can-data-ever-know-who-we-really-are

Call for Proposal: Big Data for Development – Initial Field Studies

sumandro — 2016-04-28T07:28:23Z

The Centre for Internet and Society, as part of a project with the University of Manchester and University of Sheffield, is inviting calls from researchers to undertake a brief initial study of a specific instance of use of big data for development in India. This is an exercise to build preliminary understanding of the landscape of big data for development in India, identify key research questions and priorities, and start developing connections with researchers interested in the field. The studies will be 6 weeks long - running from May to June 2016 - and the researchers are expected to produce a 3,000 words long report. We will support three field studies.

Study Process and Deliverable

The researcher is expected to propose and undertake a 6 weeks long study – starting from May 09 and ending on June 17 – of an instance of big data is being used to inform, target, operationalise, monitor, or support developmental and/or humanitarian activity in India.

During this period, the researcher is expected to interview 4-5 persons directly involved in the big data for development project concerned, and 2-3 other persons to get a wider sense of the context of the project.

By the end of the 6 weeks period, the researcher is expected to submit a 3,000 words long report. The report will be commented upon by Prof. Richard Heeks (University of Manchester), Dr. Christopher Foster (University of Sheffield), and Sumandro Chattapadhyay (CIS), and revised accordingly during the last weeks of June.

The individual reports will be published independently and as part of the larger project report, under Creative Commons Attribution 4.0 International license. The authors will be attributed appropriately.

All researchers will take part in a work-in-progress meeting (held over internet) during last week of May or first week of June.

Research Questions

The interviews will focus on the following topics:

Innovation: What is the nature of the innovation being done by the use of big data? What technical systems and/or applications are being deployed and replaced/superceded? Who are key actors in this innovation process?
Implementation: What is the grounded experience of implementing the big data technology? What are the key enablers and constraints being faced, both in the data collection stage, and the analysis and decision making stage?
Value: What is the value being created, and how is it understood? Is it organisational value, or socio-economic value? Who is gaining this value?
Ethics: What ethical concerns are emerging? Do they involve concerns about data quality, representation, privacy, or security? Is there concerns about a data divide being created among people who are represented in data and who are not, or among people who can gain value from the data and who cannot?

Application, Eligibility, and Remuneration

Please submit the following documents to apply:

Proposal: A one page note on the big data for development project that you would like to study. Please share a brief description of the project and how you will study it, including the name/designation of key people you will speak to.
Writing Sample: An article or a collection of articles, of not more than 8,000 words length in total.
CV: A short CV, two pages or less.

Please e-mail the documents to raw[at]cis-india[dot]org by Wednesday, May 04, 2016.

There is no eligibility criteria for submitting proposals. However, we will prioritise researchers living and studying big data for development projects in non X-class cities, that is in cities other than Ahmedabad, Bangalore, Chennai, Delhi, Hyderabad, Kolkata, Mumbai, and Pune.

We will select three researchers, and will offer Rs. 35,000 to each of them for this study. The amount will be paid in a single installment, after the draft field study report is submitted for comments.

For more details visit https://cis-india.org/jobs/call-for-proposal-big-data-for-development-field-studies

Brindaalakshmi.K - Gendering of Development Data in India: Beyond the Binary

Brindaalakshmi.K — 2020-06-30T10:26:40Z

This report by Brindaalakshmi.K seeks to understand the gendering of development data in India: collection of data and issuance of government (foundational and functional) identity documents to persons identifying outside the cis/binary genders of female and male, and the data misrepresentations, barriers to accessing public and private services, and informational exclusions that still remain. Sumandro Chattapadhyay edited the report and Puthiya Purayil Sneha offered additional editorial support. This work was undertaken as part of the Big Data for Development network supported by International Development Research Centre (IDRC), Canada.

Part 1 - Introduction, Research Method, and Summary of Findings: Download (PDF)

Part 2 - Legal Rights and Enumeration Process: Download (PDF)

Part 3 - Identity Documents and Access to Welfare: Download (PDF)

Part 4 - Digital Services and Data Challenges: Download (PDF)

India has been under a national lockdown due to the global outbreak of the COVID-19 pandemic since late March 2020. Although transgender persons or individuals who do not identify with the gender of their assigned sex at birth, fall into the eligibility category for the relief measures announced by the State, the implementation of the relief measures has seen to be inefficient in different states [1] of the country [2]. Many transgender persons still do not have proper identification documents in their preferred name and gender that can help them with claiming any welfare that is available [3].

Historically, the situation of transgender persons in India has been so, even prior to the present pandemic. A qualitative research study titled Gendering of Development Data in India: Beyond the Binary was undertaken during October 2018 - December 2019, to understand the gendering of development data in India, collection of data and issuance of government (foundational and functional) identity documents to persons identifying outside the cis/binary genders of female and male, and the data misrepresentations, barriers to accessing public and private services, and informational exclusions that still remain.

The interviews for this study were conducted in late 2018 and this report was completed in the beginning of 2020, after India went through an extended national debate on and finally enactment of the Transgender Persons (Protection of Rights) Act during 2019. Three key observations from this study are presented in this blog post. Although these observations were made prior to the release of the draft rules of the new law, it is important to note that the law along with the draft rules in its present version will likely aggrevate the data and social exclusions faced by the transgender community in India.

Observation 1: The need for data has sidestepped the state’s responsibility to address the human rights of its people

The present global development agenda is to leave no one behind [4]. The effort to leave no one behind has shifted the focus of the state towards collecting data on different population groups. The design of and access to welfare programmes relies heavily on the availability of data. The impact of these programmes are again measured and understood as reflected by data. This shift in focus to data has led to further exclusion of already disenfranchised groups including the transgender community [5]. The problem with this lies in the framing of the development discourse as one that demands data as the prerequisite to access welfare benefits.

However, there are significant issues with the data on transgender persons that has been fed into different national and state-level databases, beginning with the census of 2011. For the first time, census of 2011 attempted to enumerate transgender persons. However, the enumeration of transgender persons for the census of 2011 has been severely criticised by the transgender community due to lack of

Clear distinction between sex and gender in the census data collection process,
Community consultation in designing the enumeration process, and
Inclusion of all transgender identities, among others.

However, this flawed data set is being used as the primary data for fund allocation across different states for transgender people’s inclusion, note respondents. Further, any person identifying outside the gender of their assigned sex at birth faces the additional burden of proving their gender identity to access any welfare benefit. However, cisgendered men or women are never asked to prove their gender identity. The need for data from a marginalised population group without addressing the structural problems has only led to further exclusion of this already invisible group of individuals, note respondents. Further, the Transgender Persons (Protection of Rights) Act, 2019 was passed despite the severe criticisms from the transgender community, human rights activist groups [6] and even opposition political parties [7] in India for several reasons [8].

Observation 2: Replication of existing offline challenges by digital systems in multiple data sources, continues to keep transgender persons excluded

Digitisation was supposed to remove existing offline challenges and enable more people centric systems [9]. However, digital systems seem to have replicated the existing offline challenges. In several cases, digitisation has added to the complexities involved.

The replication of challenges begins with the assumption that digital processes are the best way to collect data on transgender persons. Both level of literacy and digital literacy are low among transgender persons in India. According to a report by the National Human Rights Commission [10], nearly 50% of transgender persons have studied less than Class X. This has a significant effect on their access to different rights.

Access to mobile phones is assumed to bridge this access gap to online systems and services. However, observations from different respondents suggest otherwise. Additionally, due to their gender identity, transgender individuals face different set of challenges in procuring valid identification documents required to enter data systems, note respondents. This includes but not limited to:

Lack of standardised online or offline processes to aid in changing their documents and vary within each state in different documents.
Procuring any identification document in preferred name and gender requires existing identification documents in given name and assigned gender, in both online and offline processes. However, due to the stigma with their gender identity, transgender persons often run away from home with no identification document in their assigned name and gender.
With or without an existing ID document, individuals have to go through a tedious offline legal process to change their name and gender on different documents.
Information on such processes, digital or otherwise are usually available only to individuals who are educated or associated with a non-profit organisation working with the community. The challenges are higher for individuals with neither.

Observation 3: Private big data is not good enough as an alternative source of evidence for designing welfare services for transgender persons

Globally, public private partnerships for big data are being pushed through different initiatives like Data Collaboratives [11] and UN Global Pulse [12], among others. These private partnerships are being seen as key to using big data for official statistics, which can then aid in making welfare decisions [13]. However, the respondents note that the different private big data sources are not good enough to make welfare decisions for various reasons including but not limited to:

Dependency on government documents: Access to any private service system like banking, healthcare, housing or education by any individual requires verification using some proof of identity. The discrimination and challenges in procuring government issued identification documents impacts the ability of transgender persons to enter private data systems. This in turn impacts their access to services.
Misrepresentation in data: The dependency of private services on government issued documents / government recorded data, and hierarchy among such documents/data and the continued misrepresentation of transgender people, impacts the big data generated by private service providers. Due to the stigma faced, many transgender persons avoid using public healthcare systems for other medical conditions. The heavy dependency on private health care and lower usage of public health systems, results in insufficient big data on transgender persons, created by both public and private medical care and hence cannot be used to design health related welfare services.
Social media data issues: Different websites and apps also use social media login as the ID verification mechanism. Since not all transgender persons are out to their family and friends about their gender identity, they often tend to have multiple social media accounts with different names and gender to protect their identity. When open about their gender identity, harassment and bullying of transgender persons with violent threats or sexually lucid remarks are quite common on social media platforms. Online privacy therefore continues to be a serious concern for them. Disclosing their transgender status also enables the system to predict user patterns of a vulnerable group with potential for abuse, note respondents.

In conclusion, the present global pandemic has further amplified the inherent flaws in the present data-driven welfare system in the country and its impacts on a marginalised population group like transgender persons in the country. Globally, gender in development data is seen in binary genders of male and female, leaving behind transgender individuals or those who do not identify with the gender of their assigned sex at birth. So the dominant binary gender data conversation is in fact leaving people behind. With the regressive Transgender Persons (Protection of Rights) Act of 2019 and its rules, this inadequacy in the global development agenda related to gender equality is felt at an amplified scale.

Building on the work of Dr. Usha Ramanathan, a renowned human rights activist, I say that data collection and monitoring systems that tag, track, and profile transgender persons placing them under surveillance, have consequences beyond the denial of services, and enter into the arena of criminalising for being beyond the binary [14]. The vulnerabilities of their gender identity exacerbates the threat to freedom. With their freedom threatened, expecting people to be forthcoming about self-identifying themselves in their preferred name and gender, so as to ensure that they are counted in data-driven development interventions and can thus access their constitutionally guaranteed rights, goes against the very idea of sustainable development and human rights.

References

[1] Kumar. V (2020, May 13). In Jharkhand, a Mockery of 'Right to Food' as Lockdown Relief Measures Fail to Deliver. The Wire. Retrieved from: https://thewire.in/food/lockdown-jharkhand-hunger-deaths-corruption-food

[2] Manoj. C.K. (2020, April 24). COVID-19: Thousands pushed to starvation due to faulty biometric system in Bihar. DownToEarth. Retrieved from: https://www.downtoearth.org.in/news/food/covid-19-thousands-pushed-to-starvation-due-to-faulty-biometric-system-in-bihar-70681

[3] G. Ram Mohan. (2020, May 01). Eviction Fear Heightens as Lockdown Signals Loss of Livelihood for Transgender People. The Wire. Retrieved from: https://thewire.in/rights/transgender-people-lockdown-coronavirus

[4] UN Statistics (2016). The Sustainable Development Goals Report 2016. United Nations Statistics. Retrieved from: https://unstats.un.org/sdgs/report/2016/leaving-no-one-behind

[5] Chakrabarti. A (2020, April 25). Visibly Invisible: The Plight Of Transgender Community Due To India's COVID-19 Lockdown. Outlook. Retrieved from: https://www.outlookindia.com/website/story/opinion-visibly-invisible-the-plight-of-transgender-community-due-to-indias-covid-19-lockdown/351468

[6] Knight Kyle. (2019, December 05). India’s Transgender Rights Law Isn’t Worth Celebrating. Human Rights Watch. Retrieved from: https://www.hrw.org/news/2019/12/06/indias-transgender-rights-law-isnt-worth-celebrating

[7] Dharmadhikari Sanyukta. (2019). Trans Bill 2019 passed in Lok Sabha: Why the trans community in India is rejecting it. The News Minute. August 05. Retrieved from: https://www.thenewsminute.com/article/trans-bill-2019-passed-lok-sabha-why-trans-community-india-rejecting-it-106695

[8] Editorial. (2018, December 20). Rights, revised: on the Transgender Persons Bill, 2018. The Hindu. Retrieved from: https://www.thehindu.com/opinion/editorial/rights-revised/article25783926.ece

[9] Ministry of Electronics and Information Technology, Government of India. (2018). National e-Governance Plan. Retrieved from: https://meity.gov.in/divisions/national-e-governance-plan

[10] Kerala Development Society. (2017, February). Study on Human Rights of Transgender as a Third Gender. Retrieved from: https://nhrc.nic.in/sites/default/files/Study_HR_transgender_03082018.pdf

[11] Verhulst, S. G., Young, A., Winowatan, M., & Zahuranec, A. J. (2019, October). Leveraging Private Data for Public Good: A Descriptive Analysis and Typology of Existing Practices. GovLab, Tandon School of Engineering, New York University. Retrieved from: https://datacollaboratives.org/static/files/existing-practices-report.pdf

[12] Kirkpatrick, R., & Vacarelu, F. (2018, December). A Decade of Leveraging Big Data for Sustainable Development. UN Chronicle, Vol. LV, Nos. 3 & 4. Retrieved from: https://unchronicle.un.org/article/decade-leveraging-big-data-sustainable-development

[13] See [11].

[14] Ramanathan. U. (2014, May 02). Biometrics Use for Social Protection Programmes in India Risk Violating Human Rights of the Poor. UNRISD. Retrieved from: http://www.unrisd.org/sp-hr-ramanathan

For more details visit https://cis-india.org/raw/brindaalakshmi-k-gendering-development-data-india

Big Data Governance Frameworks for 'Data Revolution for Sustainable Development'

Meera Manoj — 2016-07-05T13:13:32Z

A key component of the process to achieve the Sustainable Development Goals is the call for a global 'data revolution' to better understand, monitor, and implement development interventions. Recently there has been several international proposals to use big data, along with reconfigured national statistical systems, to operationalise this 'data revolution for sustainable development.' This analysis by Meera Manoj highlights the different models of collection, management, sharing, and governance of global development data that are being discussed.

1. What are the Sustainable Development Goals?

2. The Need for a Data Revolution

3. Big Data: Characteristics and Use for Development

3.1. Characteristics of Big Data

3.2. Using Big Data for Development

4. Sustainable Development and Data Rights

5. Governance Frameworks Proposed

5.1. UN Sustainable Development Solutions Network

5.2. The UN DATA Revolution Group

5.3. Organization for Economic Co-Operation and Development

5.4. The Global Partnership for Sustainable Development of Data

5.5. The World Economic Forum (WEF)

5.6. Dr. Julia Lane - A Quadruple Data Helix

5.7. Data Pop Alliance

6. Conclusion

7. Endnotes

8. Author Profile

Speaking on Big Data, Dan Ariely commented that, "Everyone talks about it, nobody really knows how to do it, and everyone thinks everyone else is doing it, so everyone claims they are doing it" [1]. This offers a useful insight into the lack of adequate discourse on the kind of governance and accountability frameworks that are needed to facilitate the developmental, sustainable, and responsible uses of big data.

In light of the recent international proposals to use big data to track the Sustainable Development Goals, this paper highlights the different models of management, sharing, and governance of data that are being discussed, and concurrently, how they conceptualise the various rights around big data and how are they to be protected.

1. What are the Sustainable Development Goals?

The Sustainable Development Goals, otherwise known as the Global Goals, build on the Millennium Development Goals (MDGs). Adopted on 1 January 2016, these universally applicable 17 goals of the 2030 Agenda for Sustainable Development, seek to end all forms of poverty, fight inequalities, tackle climate change and address a range of social needs like education, health, social protection and job opportunities over the next 15 years [2].

Source: UN Data Revolution Group, A World that Counts, 2014, p.12.

2. The Need for a Data Revolution

An overwhelming cause of concern regarding the precursor to the SDGs, the MDGs, is the data unavailability to monitor their progress. For instance, the figure below indicates that there is no five-year period when the availability of MDG related data is more than 70% of what is required. Entire groups of people and key issues remain invisible [3]. Lack of data is not only a problem for global statisticians, but also for people whose needs and demands remain invisible due to lack of quantitative representation of the same. For instance, the incidences of gender related crimes when not recorded could lead to a misconception on the achievement of the MDG of gender equality.

Source: UN, Sustainable Development Goals.

As the new goals (SDGs) cover a wider range of issues it is clear that a far higher level of detail is required. To this effect the High-Level Panel of Eminent Persons on the post-2015 agenda has called for a "data revolution for sustainable development" [4].

The world is experiencing a Data Revolution and a "data deluge." One estimate has it that 90% of the data in the world has been created in the last 2 years. As Eric Schmidt of Google in 2010 famously said, "There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days [5].

In its report A World that Counts, the UN Data Revolution Group defines the data revolution as an explosion in the volume of data, the speed with which data are produced, the number of producers of data, the dissemination of data, and the range of things on which there is data, coming from new technologies such as mobile phones and the “internet of things”, and from other sources, such as qualitative data, citizen-generated data and perceptions data [6].

This data revolution in the context of sustainable development has been defined by the UN Secretary General’s Independent Expert Advisory Group (IEAG) as follows:

[T]he integration of data coming from new technologies with traditional data in order to produce relevant high‐quality information with more details and at higher frequencies to foster and monitor sustainable development. This revolution also entails the increase in accessibility to data through much more openness and transparency, and ultimately more empowered people for better policies, better decisions and greater participation and accountability, leading to better outcomes for the people and the planet [7].

The majority of such “data coming from new technologies” is what can be called big data. It is data being generated in real-time, in high velocity and volume, in a variety of forms and formats, and on an increasing range of phenomenon that are being mediated by digital technologies – from governance to human communication. Further, a good part of such big data is not about the content of the phenomenon concerned but about its process – for example, Call Detail Records are generated for each mobile phone call a person makes and it contains data about the process of the call (time, location, duration, recipient, etc.) but not about the content of the call. Big data about various governmental and human processes are becoming a crucial instrument for documenting and monitoring of the same.

3. Big Data: Characteristics and Use for Development

3.1. Characteristics of Big Data

The simplest definition of big data is that it is a dataset of more than 1 petabyte. The US Bureau of Labour Statistics terms it to be non-sampled data, characterized by the creation of databases from electronic sources whose primary purpose is something other than statistical inference [8].

The characteristics which broadly distinguish Big Data are sometimes called the “3 V’s”: more volume, more variety and higher rates of velocity [9]. Big data sources generally share some or all of these features [10]:

Digitally generated,
Passively produced,
Automatically collected,
Geographically or temporally trackable, and
Continuously analysed.

Increasingly, Big Data is recognised as creating "new possibilities for international development" [11]. It could provide faster, cheaper, more granular data and help meet growing and changing demands. It was claimed, for example, that "Google knows or is in a position to know more about France than INSEE" [12], its highly resourceful national statistical agency. To illustrate, Global Pulse gives the example of a hypothetical small household facing soaring commodity prices, particularly food and fuel [13]. They have the options of:

Getting part of their food at a nearby World Food Programme distribution centre,
Reducing mobile usage,
Temporarily taking their children out of school,
Calling a health hotline when children show signs of malnutrition related diseases, and
Venting about their frustration on social media.

Such a systemic shock of food insecurity will prompt thousands of households to react in roughly similar ways. These collective behavioural changes may show up in different digital data sources:

WFP might record that it serves twice as many meals a day,
The local mobile operator may see reduced usage,
UNICEF data may indicate that school attendance has dropped,
Health hotlines might see increased volumes of calls reporting malnutrition, and
Tweets mentioning the difficulty to “afford food” might begin to rise.

Thus the power of real-time, digital data to predict paths for development is immense. Amassing such a large volume of data which tracks practically every aspect of social behavious can revolutionize the field of official statistics and policy making.

Two points to be noted are: 1) all these data sources are not available for comparison in the real-time by default, so one task before using big data in developmental work is to make data from different sources available across agencies and make them comparable, and 2) finding repeating patterns within large data sets, sourced from varied origins, can not only allow for monitoring but also (statistically) predicting future possibilities and implications for development action.

3.2. Using Big Data for Development

There are several international organizations attempting to use such data.

Global Pulse, a United Nations initiative, launched by the Secretary-General in 2009, seeks to leverage innovations in digital data, rapid data collection and analysis to help decision-makers gain a real-time understanding of how crises impact vulnerable populations. To this end, Global Pulse is establishing an integrated, global network of Pulse Labs, anchored in Pulse Lab New York, to pilot the approach at country level [14].

The Global Working Group on Big Data for Official Statistics, created in May 2014, pursuant to Statistical Commission, makes an inventory of ongoing activities and examples regarding the use of big data, addresses concerns related to methodology, human resources, quality and confidentiality, and develops guidelines on classifying various types of big data sources [15].

There have been applications even on a national and individual level. For instance, in 2013, various sources reported that the CIA had admitted to the “full monitoring of Facebook, Twitter, and other social networks” to identify links between events and sequences or paths leading to national security threats, ultimately leading to forecasting future activities and events [16].

In the field of conflict prevention is the emerging applications to map and analyse unstructured data generated by politically active Internet use by academics, activists, civil society organizations, and even general citizens. In reference to Iran’s post-election crisis beginning in 2009, it is possible to detect web-based usage of terms that reflect a general shift from awareness towards mobilization, and eventually action within the population [17].

The "Big Data, Small Credit" report proposes that financial inclusion can be promoted by allowing consumers with mobile phones to access credit formally as customers [18].

At a national level, the biggest challenge for most big data projects is the limited or restricted access the government agencies have to potential big data sets owned by the private sector [19]. The overall consensus is that Big Data to track SDGs must complement traditional data sources [20]. This is because big data may not always be available for the entire population, or include a diverse enough sample of the population. Moreover most big data projects measure development indicators through a correlation which may not always be correct unlike official data. For instance big data might help in predicting lowered household income through reducing mobile bills while traditional data directly collects income statistics.

In a survey by the Global Working Group on Big Data for Official Statistics [21], it was found that only a few countries have developed a long-term vision for the use of big data, while many are formulating a big data strategy. Most countries have not yet defined business processes for integrating big data sources and results into their work and do not have a defined structure for managing big data projects.

Thus there exists a need to identify a governance framework for big data for sustainable development, not only at national level, but also at the international level.

4. Sustainable Development and Data Rights

Any discussion on governance frameworks would be incomplete without defining the kind of data rights they must seek to protect.

In the famous parable of the six blind men and the elephant they conclude that the elephant is like a wall, snake, spear, tree, fan or rope, depending upon where they touch. Similarly Internet experiences of individual users (what they touch) often contrast drastically with different views (what they conclude) on what would constitute data rights.

The IEAG in its report has identified the following set of data related rights, but has not defined any actual framework or process for ensuring them (yet) [22]:

Right to be counted,
Right to an identity,
Right to privacy and to ownership of personal data,
Right to due process (for example when data is used as evidence in proceedings, or in administrative decisions),
Freedom of expression,
Right to participation,
Right to non-discrimination and equality, and
Principles of consent.

Personal data is broadly defined as "any information relating to an identified or identifiable individual" [23]. Often primary data producers (users of services and devices generating data) are unaware of individual privacy infringements [24].

A survey by the Global Working Group on Big Data for Official Statistics found that only a few countries have a specific privacy framework for big data, while most apply the privacy framework for traditional statistics to big data as well [25].

Conventionally, safeguards against the re-use of big data to protect data rights have involved the “anonymization” or “de-identification” of data, to conceal individual identities. Global Pulse, for instance, is putting forth the concept of Data Philanthropy, whereby "corporations take the initiative to anonymize (strip out all personal information) their data sets and provide this data to social innovators to mine the data for insights, patterns and trends in real-time or near real-time" [26]. There however exists a debate on whether data can actually be anonymized effectively. Several state that data can never be effectively de-anonymized due to technological challenges [27]. For instance, when the New York City government released de-anonymised data sets of New York cab drivers were made re-identifiable by approaching a separate method. Within less than 2 hours work, researchers knew which driver drove every single trip in this entire dataset. It would be even be easy to calculate drivers’ gross income, or infer where they live [28].

Even the OECD opines that the current model of limiting identifiability of individuals is unsustainable. It recommends moving towards one where the focus is on transparency around how data is being used, rather than preventing specific types of use, stating that - "research funding agencies and data protection authorities should collaborate to develop an internationally recognized framework code of conduct covering the use of new forms of personal data, particularly those generated via network communication. This framework, built on best practice procedures for consent from data subjects, data sharing and re-use, anonymization methods, etc., could be adapted as necessary for specific national circumstances" [29].

Thus, there is a push for the arguement that the historical approaches to protecting privacy and confidentiality — namely, informed consent and anonymity — no longer hold [30]. Some have even suggested using big data itself to keep track of user permissions for each piece of data to act as a legal contract [31].

There is an overall consensus that any legal or regulatory mechanisms set up to mobilise the 'data revolution for sustainable development' should protect the data rights of the people [32], without any clear agreement on what these rights may be.

5. Governance Frameworks Proposed

A largely unanswered question that is posed in light of the emerging consensus on the use of Big Data for monitoring SDGs is within what sort of governance frameworks these data collection and analysis methods will operate. Methods of collection and the key actors involved in data analysis, management, storage and coordination. The role of NGOs and CSOs, if any, within these systems must be delineated. Certain key global organizations and eminent researchers have suggested the following models.

5.1. UN Sustainable Development Solutions Network

In 2012, the UN Secretary-General launched the UN Sustainable Development Solutions Network (SDSN) to mobilize global scientific and technological expertise to promote practical problem solving for sustainable development, including the design and implementation of the Sustainable Development Goals (SDGs) [33]. It has proposed the following.

Collection

The Inter-Agency and Expert Group on Sustainable Development Goal Indicators (IAEGSDG) and the United Nations Statistical Commission are to establish roadmaps for strengthening specific data collection tools that enable the monitoring of SDG indicators.

Analysis

Based on discussions with a large number of statistical offices, including Eurostat, BPS Indonesia, the OECD, the Philippines, the UK, and many others, 100 is recommended to be the maximum number of global indicators to analyse data for which NSOs can report and communicate effectively in a harmonized manner. This conclusion was strongly endorsed during the 46th UN Statistical Commission and the Expert Group Meeting on SDG indicators [34].

Specialist indicators developed by thematic communities must be used for data analysis as they include input and process metrics that are helpful complements to official indicators, which tend to be more outcome-focused. For example, the UN Inter-Agency Group on Child Mortality Estimation has developed a specialist hub responsible for analysing, checking, and improving mortality estimation. This is a leading source for child morality information for both governments and non-governmental actors [35].

Research arms of private companies such as Microsoft Research, IBM research, SAS, and R&D arms of telecom companies could directly partner with official statistical systems to share sophisticated analysing techniques [36].

Management

Four levels of monitoring, national, regional, global, and thematic, should be "organized in an integrated architecture" [37].

Countries must decide individually whether official data must be complemented with non-official indicators from big data which can add richness to the monitoring of the SDGs.

Where possible, regional monitoring should build on existing regional mechanisms, such as the Regional Economic Commissions, the Africa Peer Review Mechanism, or the Asia-Pacific Forum on Sustainable Development [38].

To coordinate thematic monitoring under the SDGs, each thematic initiative may have one or more lead specialist agencies or “custodians” as per the IAEG-MDG monitoring processes. Lead agencies would be responsible for convening multi-stakeholder groups, compiling detailed thematic reports, and encouraging ongoing dialogues on innovation. These thematic groups can become testing grounds in launching a data revolution for the SDGs, trialling new measurements and metrics that in time can feed into the global monitoring process with annual reports [39].

Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.
Source: UN Sustainable Development Solutions Network, Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs, 2015, p.3.

Role of NSOs

Monitoring the SDG agenda will require substantive improvements in national statistical capacity. Assessments of existing capacity to fulfil SDG monitoring expectations must be undertaken and needs be integrated into National Strategies for the Development of Statistics (NSDSs) [40].

Coordination

A Global Partnership for Sustainable Development Data must be established and a World Forum on Sustainable Development Data be convened in 2016 to create mechanisms for ongoing collaboration and innovation.

A high-level, powerful group of businesses and states must convene the various data and transparency sustainable development initiatives under one umbrella.

To ensure comparability, Global Monitoring Indicators must be harmonized across countries by one lead technical or specialist agency which will additionally coordinate data standards and collection and provide technical support.

The following table indicates the suggested Lead Agencies for individual SDGs [41].

Number	Sustainable Development Goal	Lead Agencies
1.	No Poverty	World Bank, UNDP, UNSD, UNICEF, ILO, FAO, UN-Habitat, UNISDR, WHO, CRED, UNFPA, and UN Population Division
2.	No Hunger	FAO, WHO, UNICEF, and Internal Fertilizer Industry Associaton (IFA)
3.	Good Health	WHO, UN Population Division, UNICEF, World Bank, GAVI, UN AIDS, and UN-Habitat
4.	Quality Education	UNESCO, UNICEF, and World Bank
5.	Gender Equality	UNICEF, UN Women, WHO, UNSD, ILO, UN Population Division, and UNFPA
6.	Clean Water and Sanitation	WHO/UNICEF Joint Monitoring Programme (JMP), FAO, UN Water, and UNEP
7.	Renewable Energy	Sustainable Energy for All, IEA, WHO, World Bank, and UNFCC
8.	Good Jobs and Economic Growth	IMF, World Bank, UNSD, and ILO
9.	Innovation and Infrastructure	World Bank, OECD, UNIDO, UNFCC, UNESCO, and ITU
10.	Reduced Inequalities	UNSD, World Bank, and OECD
11.	Sustainable Cities and Communities	UN-Habitat, Global City Indicators Facility, WHO, CRED, UNISDR, FAO, and UNEP
12.	Responsible Consumption	EITI, UNCTAD, UN Global Compact, FAO, UNEP Ozone Secretariat, WBCSD, GRI, IIRC, and Global Compact
13.	Climate Action	OECD DAC, UNFCCC, and IEA
14.	Life below Water	UNEP-WCMC, IUCN, and FMC
15.	Life on Land	FAO, UNEP, IUCN, and UNEP- WCMC
16.	Peace and Justice	UNODC, WHO, UNOCHA, UNCHR, IOM, OCHA, OECD, UN Global Compact, EITI, UNCTAD, UNICEF, UNESCO, and Transparency International
17.	Partnership for the Goals	BIS, IASB, IFRS, IMF, WIPO, WTO, UNSD, OECD, World Bank, OECD DAC, and SDSN

5.2. The UN DATA Revolution Group

The group constituted by the UN Secretary-General Ban Ki-moon in August 2014, is an Independent Expert Advisory Group with the aim of making concrete recommendations on bringing about a 'data revolution for sustainable development' [42]. In its report, A World that Counts, it makes the following recommendations [43].

Collection

Clear standards on data collection methods must be developed based on the UN Fundamental Principles of Official Statistics. Periodic audits must be conducted by professional and independent third parties to ensure data quality.

Governments, civil society, academia and the philanthropic sector must work together strengthening statistical literacy so that all people have capacity to input into and evaluate the quality of data.

Social entrepreneurs, private sector, academia, media, civil society and other individuals and institutions must be engaged globally with incentives (prizes, data challenges) to encourage data sharing.

Analysis

A SDGs Analysis and Visualisation Platform is to be set up for fostering private-public partnerships and community-led peer-production efforts for data analysis.

A dashboard on ”the state of the world” will engage the UN, think-tanks, academics and NGOs in analysing, and auditing data.

Academics and scientists are to analyse data to provide long-term perspectives, knowledge and data resources at all levels.

The “Global Forum of SDG-Data Users” will ensure feedback loops between data producers, processors and users to improve the usefulness of data and information produced.

A “SDGs data lab” to support the development of a first wave of SDG indicators is to be established mobilizing key public, private and civil society data providers, academics and stakeholders working with the Sustainable Development Solutions Network.

Storage

A “world statistics cloud” will store data and metadata produced by different institutions but according to common standards, rules and specifications.

Role of NSOs

Civil society organisations must share data and processing methods with private and public counterparts on the basis of agreements. They must hold governments and companies accountable using evidence on the impact of their actions, provide feedback to data producers, develop data literacy and help communities and individuals generate and use data.

NSOs are the central players of the Data Revolution. Their autonomy must be strengthened to maintain data quality. They must abandon expensive and cumbersome production processes, incorporate new data sources like big data that is human and machine-readable, compatible with geospatial information systems and available quickly enough to ensure that the data cycle matches the decision cycle. Collaborations with the private sector can boost technical and financial investments.

Coordination

Key stakeholders must create a “Global Consensus on Data”, to adopt principles concerning legal, technical, privacy, geospatial and statistical standards. Best practices related to public data such as the Open Government Partnership (OGP) and the G8 Open Data Charter are recommended foundations for such principles.

A UN-led “Global Partnership for Sustainable Development Data” is proposed, to coordinate and broker key global public-private partnerships for data sharing [44].

A “World Forum on Sustainable Development Data” and “Network of Data Innovation Networks” will be a converging point for the data ecosystem to share ideas and experiences for improvements, innovation and technology transfer.

5.3. Organization for Economic Co-Operation and Development (OECD)

The Organisation for Economic Co-operation and Development (OECD) is an inter-governmental organization that seeks to promote policies that will improve the economic and social well-being of people globally. It has made the following proposals [45].

Collection

Data is to be collected from National statistical agencies, national and international researchers and international organisations.

Role of NSOs

By leveraging the expertise of telecommunications companies and software developers, for instance, national statistical systems could potentially reduce costs and improve the availability of data to monitor development goals [46].

Coordination

National Data Forums for Social Science Data must be created for the development of social science data for improved coordination between social scientists, data producers (national statistical agencies, government departments, large private sector businesses and sources undertaking academic direction), and data curators.

Social science research communities must contribute to national plans of action after a needs assessment [47]. Research funding agencies must collaborate at the international level for a common system for referencing datasets in research publications [48].

5.4. The Global Partnership for Sustainable Development of Data

The partnership is a global network of governments, NGOs, and businesses working to strengthen the inclusivity, trust, and innovation in the way that data is used to address the world’s sustainable development efforts [49].

Analysis

There must be a common framework for information processing. At minimum, a simple lexicon must tag each datum specifying:

What: i.e. the type of information contained in the data,
Who: the observer or reporter,
How: the channel through which the data was acquired,
How much: whether the data is quantitative or qualitative, and
Where and when: the spatio-temporal granularity of the data.

Analysis of data involves filtering relevant information, summarising keywords and categorising into indicators. This intensive mining of socioeconomic data, known as “reality mining,” can be done by: (1) Continuous analysis of real time streaming data, (2) Digestion of semi-structured and unstructured data to determine perceptions, needs and wants. (3) Real-time correlation of streaming data with slowly accessible historical data repositories.

Use of big data for developmental goals can draw upon all three techniques to various degrees depending on availability of data and the specific needs.

Role of NSOs

NSOs have a pivotal part to play in the data revolution. Countries and organizations believe that big data cannot replace traditional official statistical data as it is based more on perception than facts. To quote Winston Churchill, "Do not trust any statistics that you did not fake yourself."

For instance, a study found that Google Flu Trends, to detect influenza epidemics, predicted nonspecific flu-like respiratory illnesses well but not actual flu. The mismatch was due to popular misconceptions on influenza symptoms. This has important policy implications. Doctors using Google Flu Trends may overstock on flu vaccines or be overly inclined to diagnose normal respiratory illnesses as influenza [50].

However Big Data if understood correctly, can inform where further targeted investigation is necessary and give immediate responses to favourably change outcomes.

5.5. The World Economic Forum (WEF)

The WEF is an International Organization for Public-Private Cooperation. It engages the foremost political, business and other leaders of society to shape global, regional and industry agendas [51]. In the report titled Big Data, Big Impact: New Possibilities for International Development, it makes the following recommendations [52].

Collection

Data production and development actors include individuals, public sector and the private sector. Each produce different kinds of data that have unique requirements. The private sector maintains vast troves of transactional data, much of which is "data exhaust," or data created as a by-product of other transactions. The public sector maintains enormous datasets in the form of census data, health indicators, and tax and expenditure information. The following figure highlights the different kinds of data that each sector collects and what incentives they have to share the data along with requirements to maintain such data.

World Economic Forum - Diagram on Data Commons.
Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.4.

Business models must be created to provide the appropriate incentives for private-sector actors to share data. Such models already exist in the Internet environment. For instance companies in search and social networking profit from products they offer at no charge to end users because the usage data these products generate is valuable to other ecosystem actors. Similar models could be created in garnering Big Data for SDGs. The following flowchart illustrates how different sectors must work together to incentivise data collection and sharing.

World Economic Forum - Diagram on Global Coordination.
Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.7.

5.6. Dr. Julia Lane - A Quadruple Data Helix

Dr. Julia Lane is a Professor in the Wagner School of Public Policy at New York University; and also a Provostial Fellow in Innovation Analytics and a Professor in the Center for Urban Science and Policy [53]. She has done extensive research on the uses of big data. In her paper titled "Big Data for Public Policy: A Quadruple Data Helix," she makes the following suggestions [54].

Collection

In the future there will exist a model of a quadruple data helix for data collection which will have four strands — state and city agencies, universities, private data providers, and federal agencies.i

A new set of institution, city/university data facilities, must be established. These institutions should form the backbone of the quadruple helix, with direct connections to the private sector and to the federal statistical agencies.

Analysis

There is a need for graduate training for non-traditional students, who need to understand how to use data science tools as part of their regular employment. They must identify and capture the appropriate data, understand how data science models and tools can be applied, and determine how associated errors and limitations can be identified from a social science perspective.i

Universities can act as a trusted independent third party to process, store, analyze, and disseminate data. ii

Management

The new infrastructure must ensure that data from disparate sources are collected managed and used in a manner that is informed by end users. There are many technical challenges: disparate data sets must be ingested, their provenance determined, and metadata documented. Researchers must be able to query data sets to know what data are available and how they can be used. And if data sets are to be joined, they must be joined in a scientific manner, which means that workflows need to be traced and managed in such a way that the research can be replicated.

Coordination

The role of State and City agencies is to address immediate policy issues, rather than to build long-term data infrastructures as their mandate is to work with city data than the full spectrum of available data.

5.7. Data-Pop Alliance

Data-Pop Alliance is a global coalition on Big Data and development created by the Harvard Humanitarian Initiative, MIT Media Lab, and Overseas Development Institute that brings together researchers, experts, practitioners, and activists to promote a people-centred big data revolution through collaborative research, capacity building, and community engagement [55]. It makes the following suggestions.

Collection

The idea of shared responsibility between the public and private sector is a proposed operational principles to create a deliberative space. Mechanisms and legal frameworks must be devised for private companies to share their big data under formalized and stable arrangements instead of being compelled by ad hoc requests from researchers and policymakers.

The media too, could avoid publishing statistical data collected by unexplained methodologies by employing "statistical editors" and disseminate verified information.

Role of NSOs

For official statistics, engaging with Big Data is not a technical consideration but a political obligation. In a two tier system of official and non-official statistics, the public and investors tend to distrust official figures. For instance, the results of the 2010 census in the UK are being disputed on the basis of sewage data.

It is imperative for NSOs to retain, or regain, their primary role as the legitimate custodian of knowledge and creator of a deliberative public space to democratically drive human development [56].

6. Conclusion

The Big data frameworks provide some useful insights on monitoring mechanisms though some questions remain unanswered in each model. Key actors that have been proposed include city and state agencies like NSOs, private companies, social scientists, private individuals and international research agencies. Data analysis can be through public-private collaborations, data philanthropy, and using indicators by thematic communities.

Collection

There appears consensus across models that collection must be effected through public private partnerships while providing incentives.

Analysis

While several methods of analysis have been proposed by the Global Partnership it is unclear on who will be conducting the analysis. The UNSDSN has suggested that it be conducted by academics and scientists with Julia Lane stating it must be through public private partnerships which appear more feasible and transparent.

Role of NSOs

All frameworks agree on the pivotal role of NSOs and acknowledge them as the key players and coordinators at the national level. They must be strengthened financially, technologically and politically. Most frameworks seek to empower national agencies which will coordinate collaborations with the private sector through incentives while protecting personal data.

Coordination

Several international fora have been proposed to enable coordination while there is consensus that the NSOs. A Global Partnership for Sustainable Development Data, a Global Consensus on Data and a World Forum on Sustainable Development Data have been suggested. UN organizations appear to be suggesting more responsibility for those in the UN framework with UNSDSN giving an extensive list of lead agencies (UNDP, UN Women, Who etc) while the WEF emphasises on the private sector, Data Pop Alliance on NSOs, and Prof. Lane on State and City agencies.

On an international level countries can opt to join international organization that are being setup for the purpose. It remains to be seen whether all countries globally can achieve such a feat in a coordinated manner without infringing on data rights when unanswerable to any set international organization. The burden appears to fall on civil society and market forces within the private sector to regulate this process. For instance when a private sector company starts providing large un-anonymized data sets for government use, the privacy concerns of civil society that result in them opting for the company’s competitor’s more privacy friendly products will result in a regulation through market forces. However these forces may have disparate strengths in different contexts and countries depending on market practices and information asymmetry resulting in the lack of a uniform accountability mechanism.

7. Endnotes

[1] Dan Ariely, Facebook, January 06, 2013, https://www.facebook.com/dan.ariely/posts/904383595868.

[2] United Nations Organizations, 'Sustainable Development Goals' (United Nations Sustainable Development, 26 September 2015), http://www.un.org/sustainabledevelopment/sustainable-development-goals/, accessed 6 June 2016.

[3] Data Revolution Group, 'A World that Counts: Mobilising the Data Revolution for Sustainable Development' (November 2014), http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf, accessed 8 June 2016.

[4] High level panel on the post-2015 development agenda , 'A New Global Partnership: Eradicate Poverty and Transform Economies through Sustainable Development'(Post2015hlp,0rg, July 2012), http://www.post2015hlp.org/, accessed 8 June 2016.

[5] Gary King, 'Ensuring the Data-Rich Future of the Social Sciences' [2011] 3(2) Science, http://gking.harvard.edu/files/datarich.pdf, accessed 8 June 2016.

[6] See [3].

[7] Ibid.

[8] Michael Horrigan, 'Big Data: A Perspective from the BLS' (Amstatorg, 1 January 2013) http://magazine.amstat.org/blog/2013/01/01/sci-policy-jan2013/, accessed 4 June 2016.

[9] UN Global Pulse, 'Big Data for Development: Challenges & Opportunities' (6 May 2012) http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf, accessed 5 June 2016.

[10] Emmanuel Letouzé and Johannes Jütting, 'Official Statistics, Big Data and Human Development: Towards a New Conceptual and Operational Approach' (2014) 12(3), Data-Pop Alliance White papers Series, https://www.odi.org/sites/odi.org.uk/files/odi-assets/events-documents/5161.pdf, accessed 4 June 2016.

[11] See [9].

[12] See [10].

[13] See [9].

[14] UN Global Pulse, 'About: United Nations Global Pulse' (2016) http://www.unglobalpulse.org/about-new, accessed 7 June 2016.

[15] UN Stats, 'Global Working Group' (2014) http://unstats.un.org/unsd/bigdata/, accessed 8 June 2016.

[16] New York City Press Release, ‘Mayor Bloomberg, Police Commissioner Kelly and Microsoft Unveil New, State-of-the-Art Law Enforcement Technology that Aggregates and Analyzes Existing Public Safety Data in Real Time to Provide a Comprehensive View of Potential Threats and Criminal Activity’ (New York City, 8 August 2012), http://www1.nyc.gov/office-of-the-mayor/news/291-12/mayor-bloomberg-police-commissioner-kelly-microsoft-new-state-of-the-art-law, accessed 2 July 2016.

[17] Francesco Mancini, 'New Technology and the Prevention of Violence and Conflict' (Reliefwebint, April 2013), http://reliefweb.int/sites/reliefweb.int/files/resources/ipi-e-pub-nw-technology-conflict-prevention-advance.pdf, accessed 2 July 2016.

[18] Arjuna Costa, Anamitra Deb, and Michael Kubzansky, 'Big Data, Small Credit: The Digital Revolution and Its Impact on Emerging Market Consumers,' (Omidyar, 3 March 2013) https://www.omidyar.com/sites/default/files/file_archive/insights/Big%20Data,%20Small%20Credit%20Report%202015/BDSC_Digital%20Final_RV.pdf, accessed 2 July 2016.

[19] United Nations Economic and Social Council, 'Report of the Global Working Group on Big Data for Official Statistics' (UN Stats, 3 March 2015), http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf, accessed 8 June 2016.

[20] Ibid.

[21] Ibid.

[22] See [3].

[23] OECD, 'OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data' (23 September 1980), http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm, accessed 29 May 2016.

[24] Amir Efrati, ''Like' Button Follows Web Users' (WSJ, 18 May 2011) http://www.wsj.com/articles/SB10001424052748704281504576329441432995616, accessed 23 May 2016.

[25] See [15].

[26] Robert Kirkpatrick, 'Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience' (UN Global Pulse, 16 September 2011), http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience, accessed 4 June 2016.

[27] Ibid.

[28] Arvind Narayanan, 'No silver bullet: De-identification still doesn't work' (1 April 2016), http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf, accessed 3 July 2016.

[29] OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives,' (February 2013) http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf, accessed 2 June 2016.

[30] S. Barocas, 'The Limits of Anonymity and Consent in the Big Data Age,' in Privacy, Big Data, and the public good: Frameworks for Engagement (Cambridge University Press, 2014).

[31] A. Pentland, 'Institutional Controls: The New Deal on Data,' in Privacy, Big Data, and the public good: Frameworks for Engagement (Cambridge University Press, 2014).

[32] See [3].

[33] UN Sustainable Development Solutions Network, 'About Us: Vision and Organization' (2012) http://unsdsn.org/about-us/vision-and-organization/, accessed 2 June 2016.

[34] UN Sustainable Development Solutions Network, 'Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a data revolution for the SDGs' (12 June 2015) http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf, accessed 4 June 2016.

[35] UNICEF, 'CME Info - Child Mortality Estimates' (2014) http://www.childmortality.org/, accessed 1 June 2016.

[36] See [10].

[37] UNESCO, 'Technical report by the Bureau of the United Nations Statistical Commission (UNSC) on the process of the development of an indicator framework for the goals and targets of the post-2015 development agenda' (6 March 2015) http://www.uis.unesco.org/ScienceTechnology/Documents/unsc-post-2015-draft-indicators.pdf, accessed 3 June 2016.

[38] UN, 'The Road to Dignity by 2030: Ending Poverty, Transforming All Lives and Protecting the Planet ' (4 December 2014) http://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf, accessed 7 June 2016.

[39] Ibid.

[40] UN Sustainable Development Solutions Network, 'Data for Development: An Action Plan to Finance the Data Revolution for Sustainable Development' (10 July 2015) http://unsdsn.org/wp-content/uploads/2015/04/Data-For-Development-An-Action-Plan-July-2015.pdf, accessed 3 June 2016.

[41] See [34].

[42] UN Data Revolution Group, 'About the Independent Expert Advisory Group' (6 November 2014) http://www.undatarevolution.org/about-ieag/, accessed 4 June 2016.

[43] See [3].

[44] The Partnership has already been established, and it is developing a further framework.

[45] Organisation for Economic Co-Operation and Development), 'The Organisation for Economic Co-operation and Development (OECD): About' (2016) http://www.oecd.org/about/, accessed 2 June 2016.

[46] Organisation for Economic Co-Operation and Development, 'Strengthening National Statistical Systems to Monitor Global Goals' (2015) http://www.oecd.org/dac/POST-2015%20P21.pdf, accessed 1 June 2016.

[47] Ibid.

[48] OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives' (February 2013) http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf, accessed 2 June 2016.

[49] The Global Partnership On Sustainable Development Data, 'Who We Are: The Data Ecosystem and the Global Partnership' (2016) http://www.data4sdgs.org/who-we-are/, accessed 5 June 2016.

[50] World Economic Forum, 'Big Data, Big Impact: New Possibilities for International Development' (22 January 2012) http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf, accessed 8 June 2016.

[51] World Economic Forum, 'Our Mission: The World Economic Forum' (12 January 2016) https://www.weforum.org/about/world-economic-forum/, accessed 7 June 2016.

[52] See [50].

[53] Julia Lane, Homepage, http://www.julialane.org/.

[54] Julia Lane, 'Big Data for Public Policy: The Quadruple Helix' (2016) 8(1) Journal of Policy Analysis and Management, DOI:10.1002/pam.21921, accessed 1 June 2016.

[55] Data-Pop Alliance, 'Data-Pop Alliance: Our Mission' (May 2014) http://datapopalliance.org/, accessed 1 June 2016.

[56] See [10].

8. Author Profile

Meera Manoj is a law student at the Gujarat National Law University, Gandhinagar and has completed her first year. She is passionate about civil rights, feminism, economics in law and anything involving paneer. She aspires to travel the world and build up a vast library, with unparalleled sections on International Law and Archie comics.

For more details visit https://cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development

Big Data and Reproductive Health in India: A Case Study of the Mother and Child Tracking System

ambika — 2019-12-06T04:57:55Z

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.

Case study: Download (PDF)

Introduction

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.

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.

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.

For more details visit https://cis-india.org/raw/big-data-reproductive-health-india-mcts

Centre for Internet and Society

Ethics and Human Rights Guidelines for Big Data for Development Research

Part #1 - Review of Principles of Ethics in Biomedical Science: Download (PDF)

Part #2 - Review of Principles of Ethics in Computer Science: Download (PDF)

Part #3 - Summary of Review of Codes of Ethics for Big Data and AI: Download (PDF)

Part #4 - Extended Review of Codes of Ethics for Big Data and AI: Download (PDF)

Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?

Framing Reproductive Health as a Surveillance Question

Questioning the Information and Communications Technology for Development (ICT4D) and Big Data for Development (BD4D) Rhetoric

Potential Harms of the Data Model of Reproductive Health Provisioning

Towards a Networked State and a Neo-liberal Citizen

“Quantified Self” of the Neo-liberal Citizen

Concluding Remarks

Data bleeding everywhere: a story of period trackers

Please read the full essay on Deep Dives: Data bleeding everywhere: a story of period trackers

Sadaf Khan: Media Matters for Democracy and Twitter

CFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)

Inclusive Finance India Summit: http://inclusivefinanceindia.org/.

Can data ever know who we really are?

Please read the full essay on Deep Dives: Can data ever know who we really are?

Zara Rahman: The Engine Room, Website, and Twitter

Call for Proposal: Big Data for Development – Initial Field Studies

Study Process and Deliverable

Research Questions

Application, Eligibility, and Remuneration

Brindaalakshmi.K - Gendering of Development Data in India: Beyond the Binary

Part 1 - Introduction, Research Method, and Summary of Findings: Download (PDF)

Part 2 - Legal Rights and Enumeration Process: Download (PDF)

Part 3 - Identity Documents and Access to Welfare: Download (PDF)

Part 4 - Digital Services and Data Challenges: Download (PDF)

Observation 1: The need for data has sidestepped the state’s responsibility to address the human rights of its people

Observation 2: Replication of existing offline challenges by digital systems in multiple data sources, continues to keep transgender persons excluded

Observation 3: Private big data is not good enough as an alternative source of evidence for designing welfare services for transgender persons

References

Big Data Governance Frameworks for 'Data Revolution for Sustainable Development'

1. What are the Sustainable Development Goals?

Source: UN Data Revolution Group, A World that Counts, 2014, p.12.

2. The Need for a Data Revolution

Source: UN, Sustainable Development Goals.

3. Big Data: Characteristics and Use for Development

3.1. Characteristics of Big Data

3.2. Using Big Data for Development

4. Sustainable Development and Data Rights

5. Governance Frameworks Proposed

5.1. UN Sustainable Development Solutions Network

Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.Source: UN Sustainable Development Solutions Network, Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs, 2015, p.3.

5.2. The UN DATA Revolution Group

5.3. Organization for Economic Co-Operation and Development (OECD)

5.4. The Global Partnership for Sustainable Development of Data

5.5. The World Economic Forum (WEF)

World Economic Forum - Diagram on Data Commons. Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.4.

World Economic Forum - Diagram on Global Coordination. Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.7.

5.6. Dr. Julia Lane - A Quadruple Data Helix

5.7. Data-Pop Alliance

6. Conclusion

7. Endnotes

8. Author Profile

Big Data and Reproductive Health in India: A Case Study of the Mother and Child Tracking System

Case study: Download (PDF)

Introduction

Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.
Source: UN Sustainable Development Solutions Network, Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs, 2015, p.3.

World Economic Forum - Diagram on Data Commons.
Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.4.

World Economic Forum - Diagram on Global Coordination.
Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.7.