Centre for Internet and Society

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

Brindaalakshmi.K - Gendering of Development Data in India - Beyond the Binary #4

sumandro — 2020-06-30T10:34:03Z

For more details visit https://cis-india.org/raw/brindaalakshmi-k-gendering-of-development-data-in-india-beyond-the-binary-4

Brindaalakshmi.K - Gendering of Development Data in India - Beyond the Binary #2

sumandro — 2020-06-30T09:45:47Z

For more details visit https://cis-india.org/raw/brindaalakshmi-k-gendering-of-development-data-in-india-beyond-the-binary-2

Brindaalakshmi.K - Gendering of Development Data in India - Beyond the Binary #1

sumandro — 2020-06-30T09:42:55Z

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Breach Notifications

Admin — 2017-11-14T15:31:38Z

For more details visit https://cis-india.org/internet-governance/files/breach-notifications.pdf

Book 3: To Act : Digital AlterNatives with a Cause?

nishant — 2011-09-15T14:40:51Z

In Book 3 of the Digital AlterNatives with a Cause? collective, we enter into dialogue with some of the severest and most heated debates around digital natives and their ability to effect change. To Act collides with the discourse on young people’s ability and role in technology mediated processes of change, heads-on. It deliberates on some very dense questions about how digital natives execute their visions of change using new forms of mobilisation of resources and sharing/production of information.

For more details visit https://cis-india.org/digital-natives/dnbook3

Book 2: To Think: Digital AlterNatives with a Cause?

nishant — 2011-09-15T14:35:43Z

We started the Digital Natives with a Cause? Knowledge programme, with a series of questions, which were drawn from popular discourse, research, practice, policy and experiences of people engaging with questions of youth, technology and change. Our ambition was to consolidate existing knowledge and to look at knowledge gaps which can be addressed in order to build new frameworks to understand the role that digital natives see themselves playing in their own understanding and vision of change. This Book 2 To Think, takes up the challenge of constructing new approaches and each essay in this book, through case-studies, analyses and divergent perspectives, offers a novel way of understanding processes of technology mediated citizen-driven change.

For more details visit https://cis-india.org/digital-natives/dnbook2

Bit by byte protecting her privacy

Admin — 2018-07-29T01:46:38Z

The Srikrishna committee draft law on data protection is days away. Here’s a bucket list of issues that will matter

The article by Mihir Dalal and Anirban Sen was published in Livemint on July 26, 2018. Amber Sinha was quoted.

In an era dominated by “free” platforms such as Google, Facebook and Amazon, among others, data privacy had largely been considered an academic matter. However, in the past one year that notion has changed forever, bringing data privacy to the fore, as one of the defining issues of the internet, both in India and abroad.

Last August, the Supreme Court ruled that privacy was a fundamental right under the Constitution of India. Concomitantly, the debate over Aadhaar and its potential misuse picked up steam on the back of reports about data breaches in the biometric ID system though these reports were denied by the Unique Identification Authority of India, which built Aadhaar. (The apex Court will deliver its verdict on petitions that have challenged the constitutional validity of Aadhaar and its legal framework)

Globally, Facebook came under severe criticism after it was revealed that the social media giant had compromised user data in the run up to the US elections. Finally, in May, Europe introduced its landmark data privacy law, General Data Protection Regulation (GDPR), which has put users in control of their data through various measures.

The stage is now set for the much-delayed draft law on data protection, which is expected to be submitted soon by the 10-member panel headed by former Supreme Court justice B.N. Srikrishna.

The committee, which had been set up last July, has attracted criticism from some quarters. Earlier this month, more than 150 lawyers, activists and journalists, among others, wrote to the Srikrishna committee, complaining about the lack of transparency in its process, the lack of diversity in the views held by members of the committee, besides other issues. In an earlier letter in November last, activists, lawyers and others had alleged that too many members of the committee held pro-Aadhaar views. Some experts believe that the mandate of the committee was flawed to begin with. “Given that personal information is omnipresent in so many different sectors, it is better to have a light touch legislation that deals mostly with key principles of data privacy and empowers a data commissioner to frame more detailed regulations,” said Stephen Mathias, partner, Kochhar and Co.

Last week, the Telecom Regulatory Authority of India (Trai) released a set of recommendations on data privacy that favour giving users control of their data and personal information, while severely restricting the ways in which telecom and internet companies can use customer data. Here are the major issues to watch out for in the draft data protection law.

Users vs. collectors

This broad umbrella includes mandatory consent of users for data collection, data portability, the right to be forgotten and the right to erasure. Last week, Trai gave its recommendations on some of these issues in what were considered pro-privacy and progressive suggestions. Those recommendations tracked GDPR measures. The Srikrishna committee is also expected to suggest pro-privacy measures, though the details will be all-important. The committee is also expected to define what is ‘sensitive’ or ‘critical’ data. “In India, government agencies, private entities and others collect various forms of data on individuals,” said Chetan Nagendra, partner, AZB Partners. “The committee will have to clarify what category of data is allowed to be collected and whether this should this be standardized across different entities. It will also have to standardize rules on how long is it okay to store such user-collected data.”

The flip side of user rights is the role of data repositories that collect and process user data. The committee will be required to clarify what data firms and government agencies can gather on users and what will be their responsibilities toward the usage of that data. This includes the principle of privacy by design, that is, companies must ensure by default that their platforms are designed to protect rather than exploit user data and privacy.

IndusLaw partner Namita Viswanath said that in terms of data repositories, there was a need to distinguish between a data controller and a data processor. A data controller is the user-facing platform that gathers data, whereas a data processor is often a third-party firm that provides infrastructure for the platform. “Responsibilities of user personal data should be shared between a data controller and processor. The nature and extent of liability should depend on the nature of data, the party responsible for handling data and the measures adopted, but ultimately, the data controller should most responsibility,” Viswanath said.

Regulation vs. Self-control

Given that data is such a broad-ranging topic, the Srikrishna committee will be expected to recommend who should have oversight of data-related matters. Will there be a new data protection authority? If so, what will be its scope, given that regulators, such as the RBI, Sebi and Trai, will all be affected by a privacy framework in their respective areas? And what will be the punitive measures and fines for offenders on data matters?

Some experts said the government should appoint a data protection authority. As the recent travails at Facebook show, relying solely on self-regulation of internet platforms, is a disastrous policy. But it’s unlikely that the entire burden of regulation will fall on one authority.

“Logistical problems are likely, especially in the early days, with having a top-down regulatory approach,” said Kriti Trehan, partner, Panag and Babu. “The process of training, requirement of funding and access to skilled human resources will necessitate organisational and administrative inputs. With this in mind, I believe that a co-regulatory framework for data protection will be efficient. With this approach, established parameters may guide escalation in specific instances.”

Data localisation

In April, the RBI had issued norms on the storage of payments system data, which requires digital payment providers to store data in India. That has sparked another debate over the possible stance of the Srikrishna committee. Many start-ups and firms use data servers located in overseas locations because of several reasons, including economies of scale and tax planning. “Data protection should not be confused with data access,” said Kartik Maheshwari, leader, Nishith Desai Associates. “For instance, if a firm is storing user data abroad, that should be fine as long as it is secure and access in India is provided, whenever required. Storing data locally is not necessarily the best solution from the perspective of data security as better infrastructure may be available abroad. However, the government may, in exceptional cases of sensitivity, legitimately require local storage of very narrowly defined streams of data.”

Surveillance is key

The law will also need to clearly define the contours of the contentious issue of surveillance and how to ensure that India does not end up replicating the policies in place in countries such as China, which are notorious for mass surveillance practices. Surveillance that has been legally sanctioned is part of the exceptions to regular privacy practices. The committee will have to define the parameters of these exceptions. In the case of surveillance, some experts, including Amber Sinha of Centre for Internet and Society, said that while it needs to be allowed in specific instances such as issues related to national security, a judicial system needs to be in place to protect the rights of the parties that are being put under surveillance. This, in many ways, is the heart of a very important matter.

The Aadhaar factor

The most hot-button of all issues for the committee is, of course, Aadhaar. Former UIDAI chairman Nandan Nilekani told Mint this week that “if something needs to be modified in the Aadhaar law, it will be done” by the Srikrishna committee. The changes that the committee will suggest to the Aadhaar law will go a long way in determining whether its draft law is truly pro-privacy.

For more details visit https://cis-india.org/internet-governance/news/livemint-july-26-2018-mihir-dalal-and-anirban-sen-byte-by-byte-protecting-her-privacy

Bios and Photos of Speakers for Big Data in the Global South International Workshop

praskrishna — 2015-11-06T02:01:15Z

For more details visit https://cis-india.org/internet-governance/blog/big-data-global-south-international-workshop-bios-and-photos.pdf

Bill to create bank for DNA profiling of accused coming

praskrishna — 2012-10-22T09:15:43Z

Access to data only for victim’s or suspect’s relatives.

This article by Aarthi Dhar was published in the Hindu on October 21, 2012.

A Bill to create a DNA data centre to profile people accused of serious crimes and unknown deceased is in the works. The proposal was originally mooted in 2007 but was dropped to factor in ethical, moral and legal issues on the sensitive matter.

Crafted by the Department of Biotechnology, it allows Deoxyribose Nucleic Acid (DNA) profiling for cases of culpable homicide, murder, death by negligence, miscarriage, dowry deaths, causing death of new born child, sexual assault, unnatural offences, outraging the modesty of a woman, co-habitation with a woman by deceit, adultery, enticing a married woman with criminal intent, among others.

Protecting privacy

Addressing issues related to protecting privacy of individuals, the draft Bill envisages that access to the information in the National DNA Data Bank will be restricted to those related to the victim or suspect; any individual undergoing a sentence of imprisonment or death sentence can apply to the court which convicted him, for an order of DNA testing of specific evidence under specific conditions.

The Human DNA Profiling Bill seeks to establish a DNA Profiling Board that will lay down the standards for laboratories, collection of human body substances and custody trail from collection to reporting. It also has a provision for setting up a National DNA Data Bank.

The DNA analysis of body substances that makes it possible to determine whether the source of origin of one body substance is identical to that of another, and to establish the biological relationship, if any, between two individuals.

The “forensic material” from which the DNA sample can be lifted is biological material from the body and represents intimate body samples. They include blood, semen, or any other tissue fluid.

DNA Profiling Board

As envisaged in the Bill, the DNA Profiling Board at the national level, with similar structures at the State level, will be headed by a renowned molecular biologist with the other members being from police, legal, biological and related fields.

It will deliberate and advise on all ethical and human rights issues emanating out of DNA profiling in consonance with the United Nations vis-à-vis the rights and privacy of citizens, civil liberties and issues having ethical and other social implications.

The Board will make recommendations on the use and dissemination of DNA information, ensure the accuracy, security and confidentiality of DNA and guidelines destruction of obsolete, expunged or inaccurate information.

Jail, fine for data misuse

It will also will lay down standards and procedures for establishment and functioning of DNA laboratories and Data Banks and prepare guidelines for storage of biological substances and their destruction. Any misuse of DNA data will attract imprisonment up to three years and monetary fine.

The working draft of the Bill has been sent to the Centre for Internet and Society for analysis and comments. The Citizens Forum for Civil Liberties has already opposed the proposed legislation and sought pre-emptive intervention to stop “dangerous” erosion of privacy by DNA profiling of citizens.

In a representation submitted to the National Human Rights Commission, the Forum has said DNA profiling is “undesirable, particularly as forensic DNA developments are intertwined with significant changes in legislation and contentious issues of privacy, civil liberty and social justice.”

The Forum has sought “immediate intervention to safeguard citizens’ privacy and their civil liberties, which face an unprecedented onslaught from the provisions of the DNA Profiling Bill and other related surveillance measures being bulldozed by unregulated and ungovernable technology.”

For more details visit https://cis-india.org/news/the-hindu-aarti-dhar-october-21-2012-bill-to-create-bank-for-dna-profiling-of-accused-coming

BigDog is Watching You! The Sci-fi Future of Animal and Insect Drones

maria — 2013-07-12T15:38:33Z

Do you think robotic aeroplanes monitoring us are scary enough? Wait until you read about DARPA´s new innovative and subtle way to keep us all under the microscope! This blog post presents a new reality of drones which is depicted in none other than animal and insect-like robots, equipped with cameras and other surveillance technologies.

This research was undertaken as part of the 'SAFEGUARDS' project that CIS is undertaking with Privacy International and IDRC.

Just when we thought we had seen it all, the US Defence Advanced Research Projects Agency (DARPA) funded another controversial surveillance project which makes even the most bizarre sci-fi movie seem like a pleasant fairy-tale in comparison to what we are facing: animal and insect drones.

Up until recently, unmanned aerial vehicles (UAVs), otherwise called drones, depicted the scary reality of surveillance, as robotic pilot-less planes have been swarming the skies, while monitoring large amounts of data without people´s knowledge or consent. Today, DARPA has come up with more subtle forms of surveillance: animal and insect drones. Clearly animal and insect-like drones have a much better camouflage than aeroplanes, especially since they are able to go to places and obtain data that mainstream UAVs can not.

India´s ´DARPA´, the Defence Research and Development Organisation (DRDO), has been creating UAVs over the last ten years, while the Indian Army first acquired UAVs from Israel in the late 1990s. Yet the use of all UAVs in India is still poorly regulated! Drones in the U.S. are regulated by the Federal Aviation Administration (FAA), whilst the European Aviation Safety Agency (EASA) regulates drones in the European Union. In India, the Ministry of Civil Aviation regulates drones, whilst the government is moving ahead with plans to replace the Directorate General of Civil Aviation (DGCA) with a Civil Aviation Authority. However, current Indian aviation laws are vague in regards to data acquired, shared and retained, thus not only posing a threat to individual´s right to privacy and other human rights, but also enabling the creation of a secret surveillance state.

The DRDO appears to be following DARPA´s footsteps in terms of surveillance technologies and the questions which arise are: will animal and insect drones be employed in India in the future? If so, how will they be regulated?

BigDog/LS3

Apparently having UAVs flying above us and monitoring territories and populations without our knowledge or consent was not enough. DARPA is currently funding the BigDog project, which is none other than a drone dog, a four-legged robot equipped with a camera and capable of surveillance in disguise. DARPA and Boston Dynamics are working on the latest version of BigDog, called the Legged Squad Support System (LS3), which can carry 400 pounds of gear for more than 20 miles without refuelling. Not only can the LS3 walk and run on all types of surfaces, including ice and snow, but it also has ´vision sensors´ which enable it to autonomously maneuver around obstacles and follow soldiers in the battle field. The LS3 is expected to respond to soldiers' voice commands, such as 'come', 'stop' and 'sit', as well as serve as a battery charger for electronic devices.

BigDog/LS3 is undoubtedly an impressive technological advancement in terms of aiding squads with surveillance, strategic management and a mobile auxiliary power source, as well as by carrying gear. Over the last century most technological developments have manifested through the military and have later been integrated in societies. Many questions arise around the BigDog/LS3 and its potential future use by governments for non-military purposes. Although UAVs were initially used for strictly military purposes, they are currently also being used by governments on an international level for civil purposes, such as to monitor climate change and extinct animals, as well as to surveille populations. Is it a matter of time before BigDog is used by governments for ´civil purposes´ too? Will robotic dogs swarm cities in the future to provide ´security´?

Like any other surveillance technology, the LS3 should be legally regulated and current lack of regulation could create a potential for abuse. Is authorisation required to use a LS3? If so, who has the legal right to authorise its use? Under what conditions can authorisation be granted and for how long? What kind of data can legally be obtained and under what conditions? Who has the legal authority to access such data? Can data be retained and if so, for how long and under what conditions? Do individuals have the right to be informed about the data withheld about them? Just because it´s a ´dog´ should not imply its non-regulation. This four-legged robot has extremely intrusive surveillance capabilities which may breach the right to privacy and other human rights when left unregulated.

Humming Bird Drone


Source: HighTech Edge

TIME magazine recognised DARPA for its Hummingbird nano air vehicle (NAV) and named the drone bird one of the 50 best inventions of 2011. True, it is rather impressive to create a robot which looks like a bird, behaves like a bird, but serves as a secret spy.

During the presentation of the humming bird drone, Regina Dugan, former Director of DARPA, stated:

"Since we took to the sky, we have wanted to fly faster and farther. And to do so, we've had to believe in impossible things and we've had to refuse to fear failure."

Although believing in 'impossible things' is usually a prerequisite to innovation, the potential implications on human rights of every innovation and their probability of occurring should be examined. Given the fact that drones already exist and that they are used for both military and non-military purposes, the probability is that the hummingbird drone will be used for civil purposes in the future. The value of data in contemporary information societies, as well as government's obsession with surveillance for ´national security´ purposes back up the probability that drone birds will not be restricted to battlefields.

So should innovation be encouraged for innovation’s sake, regardless of potential infringement of human rights? This question could open up a never-ending debate with supporters arguing that it´s not technology itself which is harmful, but its use or misuse. However the current reality of drones is this: UAVs and NAVs are poorly regulated (if regulated at all in many countries) and their potential for abuse is enormous, given that ´what happens to our data happens to ourselves....who controls our data controls our lives.´ If UAVs are used to surveille populations, why would drone birds not be used for the same purpose? In fact, they have an awesome camouflage and are potentially capable of acquiring much more data than any UAV! Given the surveillance benefits, governments would appear irrational not to use them.

MeshWorms and Remote-Controlled Insects


Source: NY Daily News

Think insects are creepy? Now we can have a real reason to be afraid of them. Clearly robotic planes, dogs and birds are not enough.

DARPA´s MeshWorm project entails the creation of earthworm-like robots that crawl along surfaces by contracting segments of their bodies. The MeshWorm can squeeze through tight spaces and mold its shape to rough terrain, as well as absorb heavy blows. This robotic worm will be used for military purposes, while future use for ´civil purposes´ remains a probability.

Robots, however, are not only the case. Actual insects are being wirelessly controlled, such as beetles with implanted electrodes and a radio receiver on their back. The giant flower beetle´s size enables it to carry a small camera and a heat sensor, which constitutes it as a reliable mean for surveillance.

Other drone insects look and fly like ladybugs and dragonflies. Researchers at the Wright State University in Dayton, Ohio, have been working on a butterfly drone since 2008. Former software engineer Alan Lovejoy has argued that the US is developing mosquito drones. Such a device could potentially be equipped with a camera and a microphone, it could use its needle to abstract a DNA sample with the pain of a mosquito bite and it could also inject a micro RFID tracking device under peoples´ skin. All such micro-drones could potentially be used for both military and civil purposes and could violate individuals´ right to privacy and other civil liberties.

Security vs. Privacy: The wrong debate

09/11 was not only a pioneering date for the U.S., but also for India and most countries in the world. The War on Terror unleashed a global wave of surveillance to supposedly enable the detection and prevention of crime and terrorism. Governments on an international level have been arguing over the last decade that the use of surveillance technologies is a prerequisite to safety. However, security expert, Bruce Schneier, argues that the trade-off of privacy for security is a false dichotomy.

Everyone can potentially be a suspect within a surveillance state. Analyses of Big Data can not only profile individuals and populations, but also identify ‘branches of communication’ around every individual. In short, if you know someone who may be considered a suspect by intelligence agencies, you may also be a suspect. The mainstream argument “I have nothing to hide, I am not a terrorist’ is none other than a psychological coping mechanism when dealing with surveillance. The reality of security indicates that when an individual’s data is being intercepted, the probability is that those who control that data can also control that individual’s life. Schneier has argued that privacy and security are not on the opposite side of a seesaw, but on the contrary, the one is a prerequisite of the other. Governments should not expect us to give up our privacy in exchange for security, as loss of privacy indicates loss of individuality and essentially, loss of freedom. We can not be safe when we trade-off our personal data, because privacy is what protects us from abuse from those in power. Thus the entire War on Terror appears to waged through a type of phishing, as the promise of ´security´ may be bait to acquire our personal data.

Since the 2008 Mumbai terrorist attacks, India has had more reasons to produce, buy and use surveillance technologies, including drones. Last New Year´s Eve, the Mumbai police used UAVs to monitor hotspots, supposedly to help track down revellers who sexually harass women. The Chennai police recently procured three UAVs from Anna University to assist them in keeping an eye on the city´s vehicle flow. Raj Thackeray´s rally marked the biggest surveillance exercise ever launched for a single event, which included UAVs. The Chandigarh police are the first Indian police force to use the ´Golden Hawk´ - a UAV which will keep a ´bird´s eye on criminal activities´. This new type of drone was manufactured by the Aeronautical Development Establishment (one of DRDO's premier laboratories based in Bangalore) and as of 2011 is being used by Indian law enforcement agencies.

Although there is no evidence that India currently has any animal or insect drones, it could be a probability in the forthcoming years. Since India is currently using many UAVs either way, why would animal and/or insect drones be excluded? What would prevent India from potentially using such drones in the future for ´civil purposes´? More importantly, how are ´civil purposes´ defined? Who defines ´civil purposes´and under what criteria? Would the term change and if so, under what circumstances? The term ´civil purposes´ varies from country to country and is defined by many political, social, economic and cultural factors, thus potentially enabling extensive surveillance and abuse of human rights.

Drones can potentially be as intrusive as other communications surveillance technologies, depending on the type of technology they´re equipped with, their location and the purpose of their use. As they can potentially violate individuals´ right to privacy, freedom of expression, freedom of movement and many other human rights, they should be strictly regulated. In Europe UAVs are regulated based upon their weight, as unmanned aircraft with an operating mass of less than 150kg are exempt by the EASA Regulation and its Implementation Rules. This should not be the case in India, as drones lighter than 150kg can potentially be more intrusive than other heavier drones, especially in the case of bird and insect drones.

Laws which explicitly regulate the use of all types of drones (UAVs, NAVs and micro-drones) and which legally define the term ´civil purposes´ in regards to human rights should be enacted in India. Some thoughts on the authorisation of drones include the following: A Special Committee on the Use of All Drones (SCUAD) could be established, which would be comprised of members of the jury, as well as by other legal and security experts of India. Such a committee would be the sole legal entity responsible for issuing authorisation for the use of drones, and every authorisation would have to comply with the constitutional and statutory provisions of human rights. Another committee, the Supervisory Committee on the Authorisation of the Use of Drones (lets call this ´SCAUD´), could also be established, which would also be comprised by (other) members of the jury, as well as by (other) legal and security experts of India. This second committee would supervise the first and it would ensure that SCUAD provides authorisations in compliance with the laws, once the necessity and utility of the use of drones has been adequately proven.

It´s not about ´privacy vs. security´. Nor is it about ´privacy or security´. In every democratic state, it should be about ´privacy and security´, since the one cannot exist without the other. Although the creation of animal and insect drones is undoubtedly technologically impressive, do we really want to live in a world where even animal-like robots can be used to spy on us? Should we be spied on at all? How much privacy do we give up and how much security do we gain in return through drones? If drones provided the ´promised security´, then India and all other countries equipped with these technologies should be extremely safe and crime-free; however, that is not the case.

In order to ensure that the use of drones does not infringe upon the right to privacy and other human rights, strict regulations are a minimal prerequisite. As long as people do not require that the use of these spying technologies are strictly regulated, very little can be done to prevent a scary sci-fi future. That´s why this blog has been written.

For more details visit https://cis-india.org/internet-governance/blog/big-dog-is-watching-you

Big Democracy: Big Surveillance - A talk by Maria Xynou

maria — 2013-12-12T10:23:21Z

Next Tuesday, Maria Xynou will be presenting her latest research on surveillance in India. Come and engage in a discussion on India's controversial surveillance schemes, surveillance industry and much much more!

And so we've heard a lot about the Edward Snowden leaks and about the NSA's controversial mass surveillance projects. But what's happening in India?

It turns out that the world's largest democracy has some of the most controversial surveillance schemes in the world! Some of India's laws, schemes, projects and technologies are unbeatable when it comes to mass surveillance, censorship and control. While India may be a developing country with issues ranging from poverty to corruption, it nonetheless appears to be at the forefront of surveillance on an international level.

Join us at the Centre for Internet and Society (CIS) on 3rd December 2013 to hear about India's surveillance laws, schemes and technologies and to engage in a discussion on the potential implications. All that is required is an open mind, critical thought and a will to challenge that which has not been challenged!

We look forward to seeing you all and to hearing your thoughts, ideas and opinions!

VIDEO

For more details visit https://cis-india.org/internet-governance/events/big-democracy-big-surveillance-a-talk-by-maria-xynou

Big Democracy, Big Surveillance: India's Surveillance State

maria — 2014-02-28T10:35:09Z

In India, surveillance is on the rise by the state to tackle crime and terrorism, and private companies are eager to meet the demand.

This article by Maria Xynou was published by OpenDemocracy on 10 February 2014.

Worried about the secret, mass surveillance schemes being carried out by the NSA? While we should be, some of the surveillance schemes in the world's largest democracy, India, are arguably in the same league.

Surveillance is being globalised to the extent that even India, a country with huge poverty issues, is investing millions of dollars in creating an expansive surveillance regime. However, why would communications monitoring interest Indian authorities, when the majority of the population lives below the line of poverty and only 17% of the population has access to the Internet?

The official political motivation behind surveillance in India appears to be the government's determination to tackle terrorism in the country. The 2008 Mumbai terrorist attacks were arguably a similar landmark to the 9/11 terrorist attacks in the US, and both governments officially announced their intention to carry out surveillance as a counter-terrorism measure. However, unlike in the west, terrorist attacks in India are much more common, and the National Security Adviser reported in 2008 that 800 terrorist cells were operational in the country. With India’s history of major terror attacks in India over the last 25 years, it's easy for one to be persuaded that terrorism is actually a major threat to national security.

India's surveillance schemes

India’s surveillance programs mostly started following the 2008 Mumbai terror attacks. That was when the Ministry of Home Affairs first proposed the creation of a National Intelligence Grid (NATGRID), which will give 11 intelligence and investigative agencies real-time access to 21 citizen data sources to track terror activities. These citizen data sources will be provided by various ministries and departments, otherwise called “provider agencies”, and will include bank account details, telephone records, passport data and vehicle registration details, among other types of data.

The Ministry of Home Affairs has sought over Rs. 3,400 crore (around USD 540 million!) for the implementation of NATGRID, which aims to create comprehensive patterns of intelligence by collecting sensitive information from databases of departments like the police, banks, tax and telecoms to supposedly track any terror suspect and incident.

But NATGRID is far from India's only data sharing scheme. In 2009 the Cabinet Committee on Economic Affairs approved the creation and implementation of the Crime and Criminal Tracking Network & Systems (CCTNS), which would facilitate the sharing of databases among 14,000 police stations across all 35 states and Union Territories of India, excluding 6,000 police offices which are high in the police hierarchy. Rs. 2,000 crore (around USD 320 million) have been allocated for the CCTNS, which is being implemented by the National Crime Records Bureau under the national e-governance scheme. The CCTNS not only increases transparency by automating the function of police stations, but also provides the civil police with tools, technology and information to facilitate the investigation of crime and detection of criminals.

But apparently, sharing data and linking databases is not enough to track criminals and terrorists. As such, in the aftermath of the 2008 Mumbai terror attacks, the Indian government also implemented various interception systems. In September 2013 it was reported that the Indian government has been operating Lawful Intercept & Monitoring (LIM) systems, widely in secret. In particular, mobile operators in India have deployed their own LIM systems allowing for the so-called “lawful interception” of calls by the government. And possibly to enable this, mobile operators are required to provide subscriber verification to the Telecom Enforcement, Resource and Monitoring (TERM) cells of the Department of Telecommunications.

In the case of Internet traffic, the LIM systems are deployed at the international gateways of large Internet Service Providers (ISPs) and expand to a broad search across all Internet traffic using “keywords” and “key-phrases”. In other words, security agencies using LIM systems are capable of launching a search for suspicious words, resulting in the indiscriminate monitoring of all Internet traffic, possibly without court oversight and without the knowledge of ISPs.

India has also automated and centralized the interception of communications through the Central Monitoring System (CMS). This project was initially envisioned in 2009, following the 2008 Mumbai terror attacks and was approved in 2011. The CMS intercepts all telecommunications in India and centrally stores the data in national and regional databases. The CMS will be connected with the Telephone Call Interception System (TCIS) which will help monitor voice calls, SMS and MMS, fax communications on landlines, CDMA, video calls, GSM and 3G networks. Agencies which will have access to the CMS include the Intelligence Bureau (IB), the Central Bureau of Investigation (CBI), the Directorate of Revenue Intelligence (DRI), the Research and Analysis Wing (RAW) and the National Investigation Agency (NIA).

Unlike mainstream interception, where service providers are required to intercept communications and provision interception requests to law enforcement agencies, the Central Monitoring System will automate the entire process of interception. This means that the CMS authority will have centralized access to all intercepted data and that the authority can also bypass service providers in gaining such access. Once security agencies have access to this data, they are equipped with Direct Electronic Provisioning, filters and alerts on the target numbers, as well as with Call Details Records (CDR) analysis and data mining tools to identify the personal information of target numbers.

Given that roughly 73% of India's population uses mobile phones, this means that the Central Monitoring System can potentially affect about 893 million people, more than double the population of the United States! However, how is it even possible for Indian authorities to mine the data of literally millions of people? Who supplies Indian authorities with the technology to do this and what type of technology is actually being used?

India's surveillance industry

India has the world's second largest population, consisting of more than a billion people and an expanding middle class. Undoubtedly, India is a big market and many international companies aspire in investing in the country. Unfortunately though, along with everything else being imported into India, surveillance technologies are no exception.

Some of the biggest and most notorious surveillance technology companies in the world, such as ZTE, Utimaco and Verint, have offices in India. Even FinFisher command and control servers have been found in India. However, in addition to allowing foreign surveillance technology companies to create offices and to sell their products and solutions in the country, local companies selling controversial spyware appear to be on the rise too.

Kommlabs Dezign is an Indian company which loves to show off its Internet monitoring solutions at various ISS trade shows, otherwise known as “the Wiretapper's Ball”. In particular, Kommlabs Dezign sells VerbaNET, an Internet Interception Solution, as well as VerbaCENTRE, which is a Unified Monitoring Centre that can even detect cognitive and emotional stress in voice calls and flag them! In other words, Kommlabs Dezign makes a point that not only should we worry about what we text and say over our phones, but that we should also worry about what we sound like when on the phone.

Vehere is another Indian company which sells various surveillance solutions and notably sells vCRIMES, which is a Call Details Records (CDR) analysis system. VCRIMES is used to analyse and gather intelligence and to unveil hidden interconnections and relations through communications. This system also includes a tool for detecting sleeper cells through advanced statistical analysis and can analyse more than 40 billion records in less than 3 seconds.

Paladion Networks is headquartered in Bangalore, India and sells various Internet Monitoring Systems, Telecom Operator Interception Systems, SSL Interception and Decryption Systems and Cyber Cafe Monitoring Systems to law enforcement agencies in India and abroad. In fact, Paladion Networks even states in its website that its customers include India's Ministry of Information Technology and the U.S Department of Justice.

ClearTrail Technologies is yet another Indian company which not only sponsors global surveillance trade shows but also sells a wide range of monitoring solutions to law enforcement agencies in India and abroad. ComTrail is a solution for the centralised mass interception and monitoring of voice and data networks, including Gmail, Yahoo, Hotmail, BlackBerry, ICQ and GSM voice calls. Furthermore, ComTrail is equipped to handle millions of communications per day, correlating identities across multiple networks, and can instantly analyse data across thousands of terabytes.

ClearTrail also sells xTrail, which is a solution for the targeted interception, decoding and analysis of data traffic over IP networks and which enables law enforcement agencies to intercept and monitor targeted communications without degrading the service quality of the IP network. Interestingly, xTrail can filter based on a “pure keyword”, a URL/Domain with a keyword, a mobile number or even with just a user identity, such as an email ID, chat ID or VoIP ID.

Apparently, some the biggest challenges that law enforcement agencies face when monitoring communications include cases when targets operate from public Internet networks and/or use encryption. However, it turns out that ClearTrail's QuickTrail solution is designed to gather intelligence from public Internet networks, when a target is operating from a cyber cafe, a hotel, a university campus or a free Wi-Fi zone. This device can remotely deploy spyware into a target's computer and supports protocol decoding, including HTTP, SMTP, POP3 and HTTPS.

Additionally, QuickTrail can identify a target machine on the basis of metadata, such as an IP address, and can monitor Ethernet LANs in real time, as well as monitor Gmail, Yahoo and all other HTTPS-based communications. ClearTrail's mTrail is designed for the passive 'off-the-air' interception of GSM communications, including the interception of targeted calls from pre-defined suspect lists and the monitoring of SMS and protocol information. MTrail also identifies a target's location by using signal strength, target numbers, such as IMSI, TIMSI, IMEI or MSI SDN, which makes it possible to listen to the conversation of so-called “lawfully intercepted” calls in near real-time.

In short, it looks like India is reaching the top league when it comes to surveillance technologies, especially since many of its companies and their products appear to be just as scary as some of the most sophisticated spying gear sold by the West. India may be the world's largest (by population) democracy, but that means that it has a huge population with way too many opinions...and apparently, the private and public sectors in India appear to be joining forces to do something about it.

So do Indians have nothing to hide?

A very popular rhetoric in both India and the west is that citizens should not be concerned about surveillance because, after all, if they are not terrorists, they should have nothing to hide. However, privacy advocate Caspar Bowden has rightfully stated that this rhetoric is fundamentally flawed and that we should all indeed “have something to hide”. But is privacy just about “having something to hide”? Jacob Appelbaum has stated that this rhetoric is merely a psychological copying mechanism when dealing with security.

It's probably rather comforting and reassuring to think that we are not special or important enough for surveillance to affect us personally. But is that really up to us to decide? Unfortunately not. The very point of data mining is to match patterns, create profiles of individuals and to unveil hidden interconnections and relations. A data analyst can uncover more information about us than what we are even aware of and it is they who decide if our data is “incriminating” or not. Or even worse: in many cases it's up to data mining software to decide how “special” or “important” we are. And unfortunately, technology is not infallible.

The world's largest democracy, which is also one of the most corrupt countries in the world, is implementing many controversial surveillance schemes which lack transparency, accountability and adequate legal backing, and which are largely being carried out in secret. And to make matters worse, India lacks privacy legislation. Over a billion people in a democratic regime are exposed to inadequately regulated surveillance schemes, while a local surveillance industry is thriving without any checks or balances whatsoever. What will this mean for the global future of democracy?

For more details visit https://cis-india.org/internet-governance/blog/big-democracy-big-surveillance-indias-surveillance-state

Big Data in the Global South - An Analysis

tanvi — 2016-01-24T02:54:45Z

I. Introduction

"The period that we have embarked upon is unprecedented in history in terms of our ability to learn about human behavior." [1]

The world we live in today is facing a slow but deliberate metamorphosis of decisive information; from the erstwhile monopoly of world leaders and the captains of industry obtained through regulated means, it has transformed into a relatively undervalued currency of knowledge collected from individual digital expressions over a vast network of interconnected electrical impulses.[2] This seemingly random deluge of binary numbers, when interpreted represents an intricately woven tapestry of the choices that define everyday life, made over virtual platforms. The machines we once employed for menial tasks have become sensorial observers of our desires, wants and needs, so much so that they might now predict the course of our future choices and decisions.[3] The patterns of human behaviour that are reflected within this data inform policy makers, in both a public and private context. The collective data obtained from our digital shadows thus forms a rapidly expanding storehouse of memory, from which interested parties can draw upon to resolve problems and enable a more efficient functioning of foundational institutions, such as the markets, the regulators and the government.[4]

The term used to describe a large volume of collected data, in a structured as well as unstructured form is called Big Data. This data requires niche technology, outside of traditional software databases, to process; simply because of its exponential increment in a relatively short period of time. Big Data is usually identified using a "three V" characterization - larger volume, greater variety and distinguishably high rates of velocity. [5] This is exemplified in the diverse sources from which this data is obtained; mobile phone records, climate sensors, social media content, GPS satellite identifications and patterns of employment, to name a few. Big data analytics refers to the tools and methodologies that aim to transform large quantities of raw data into "interpretable data", in order to study and discern the same so that causal relationships between events can be conclusively established.[6] Such analysis could allow for the encouragement of the positive effects of such data and a concentrated mitigation of negative outcomes.

This paper seeks to map out the practices of different governments, civil society, and the private sector with respect to the collection, interpretation and analysis of big data in the global south, illustrated across a background of significant events surrounding the use of big data in relevant contexts. This will be combined with an articulation of potential opportunities to use big data analytics within both the public and private spheres and an identification of the contextual challenges that may obstruct the efficient use of this data. The objective of this study is to deliberate upon how significant obstructions to the achievement of developmental goals within the global south can be overcome through an accurate recognition, interpretation and analysis of big data collected from diverse sources.

II. Uses of Big Data in the Global Development

Big Data for development is the process though which raw, unstructured and imperfect data is analyzed, interpreted and transformed into information that can be acted upon by governments and policy makers in various capacities. The amount of digital data available in the world today has grown from 150 exabytes in 2005 to 1200 exabytes in 2010.[7] It is predicted that this figure would increase by 40% annually in the next few years[8], which is close to 40 times growth of the world's population. [9] The implication of this is essentially that the share of available data in the world today that is less than a minute old is increasing at an exponential rate. Moreover, an increasing percentage of this data is produced and created real-time.

The data revolution that is incumbent upon us is characterized by a rapidly accumulating and continuously evolving stock of data prevalent` in both industrialized as well as developing countries. This data is extracted from technological services that act as sensors and reflect the behaviour of individuals in relation to their socio-economic circumstances.

For many global south countries, this data is generated through mobile phone technology. This trend is evident in Sub Saharan Africa, where mobile phone technology has been used as an effective substitute for often weak and unstructured State mechanisms such as faulty infrastructure, underdeveloped systems of banking and inferior telecommunication networks.[10]

For example, a recent study presented at the Data for Development session at the NetMob Conference at MIT used mobile phone data to analyze the impact of opening a new toll highway in Dakar, Senegal on human mobility, particularly how people commute to work in the metropolitan area. ^{^[11]} A huge investment, the improved infrastructure is expected to result in a significant increase of people in and out of Dakar, along with the transport of essential goods. This would initiate rural development in the areas outside of Dakar and boost the value of land within the region.^{^[12]} The impact of the newly constructed highway can however only be analyzed effectively and accurately through the collection of this mobile phone data from actual commuters, on a real time basis.

Mobile phones technology is no longer used just for personal communication but has been transformed into an effective tool to secure employment opportunities, transfer money, determine stock options and assess the prices of various commodities.[13] This generates vast amounts of data about individuals and their interactions with the government and private sector companies. Internet Traffic is predicted to grow between 25 to 30 % in the next few years in North America, Western Europe and Japan but in Latin America, The Middle East and Africa this figure has been expected to touch close to 50%.[14] The bulk of this internet traffic can be traced back to mobile devices.

The potential applicability of Big Data for development at the most general level is the ability to provide an overview of the well being of a given population at a particular period of time.[15] This overcomes the relatively longer time lag that is prevalent with most other traditional forms of data collection. The analysis of this data has helped, to a large extent, uncover "digital smoke signals" - or inherent changes in the usage patterns of technological services, by individuals within communities.[16] This may act as an indicator of the changes in the underlying well-being of the community as a whole. This information about the well-being of a community derived from their usage of technology provides significantly relevant feedback to policy makers on the success or failure of particular schemes and can pin point changes that need to be made to status quo. [17]The hope is that this feedback delivered in real-time, would in turn lead to a more flexible and accessible system of international development, thus securing more measurable and sustained outcomes. [18]

The analysis of big data involves the use of advanced computational technology that can aid in the determination of trends, patterns and correlations within unstructured data so as to transform it into actionable information. It is hoped that this in addition to the human perspective and experience afforded to the process could enable decision makers to rely upon information that is both reliable and up to date to formulate durable and self-sustaining development policies.

The availability of raw data has to be adequately complemented with intent and a capacity to use it effectively. To this effect, there is an emerging volume of literature that seeks to characterize the primary sources of this Big Data as sharing certain easily distinguishable features. Firstly, it is digitally generated and can be stored in a binary format, thus making it susceptible to requisite manipulation by computers attempting to engage in its interpretation. It is passively produced as a by-product of digital interaction and can be automatically extracted for the purpose of continuous analysis. It is also geographically traceable within a predetermined time period. It is however important to note that "real time" does not necessarily refer to information occurring instantly but is reflective of the relatively short time in which the information is produced and made available thus making it relevant within the requisite timeframe. This allows efficient responsive action to be taken in a short span of time thus creating a feedback loop. [19]

In most cases the granularity of the data is preferably sought to be expanded over a larger spatial context such as a village or a community as opposed to an individual simply because this affords an adequate recognition of privacy concerns and the lack of definitive consent of the individuals in the extraction of this data. In order to ease the process of determination of this data, the UN Global Pulse has developed taxonomy of sorts to assess the types of data sources that are relevant to utilizing this information for development purposes.[20] These include the following sources;

Data Exhaust or the digital footprint left behind by individuals' use of technology for service oriented tasks such as web purchases, mobile phone transactions and real time information collected by UN agencies to monitor their projects such as levels of food grains in storage units, attendance in schools etc.

Online Information which includes user generated content on the internet such as news, blog entries and social media interactions which may be used to identify trends in human desires, perceptions and needs.

Physical sensors such as satellite or infrared imagery of infrastructural development, traffic patterns, light emissions and topographical changes, thus enabling the remote sensing of changes in human activity over a period of time.

Citizen reporting or crowd sourced data , which includes information produced on hotlines, mobile based surveys, customer generated maps etc. Although a passive source of data collection, this is a key instrument in assessing the efficacy of action oriented plans taken by decision makers.

The capacity to analyze this big data is hinged upon the reliance placed on technologically advanced processes such as powerful algorithms which can synthesize the abundance of raw data and break down the information enabling the identification of patterns and correlations. This process would rely on advanced visualization techniques such "sense-making tools"[21]

The identification of patterns within this data is carried out through a process of instituting a common framework for the analysis of this data. This requires the creation of a specific lexicon that would help tag and sort the collected data. This lexicon would specify what type of information is collected and who it is interpreted and collected by, the observer or the reporter. It would also aid in the determination of how the data is acquired and the qualitative and quantitative nature of the data. Finally, the spatial context of the data and the time frame within which it was collected constituting the aspects of where and when would be taken into consideration. The data would then be analyzed through a process of Filtering, Summarizing and Categorizing the data by transforming it into an appropriate collection of relevant indicators of a particular population demographic. [22]

The intensive mining of predominantly socioeconomic data is known as "reality mining" [23] and this can shed light on the processes and interactions that are reflected within the data. This is carried out via a tested three fold process. Firstly, the " Continuous Analysis over the streaming of the data", which involves the monitoring and analyzing high frequency data streams to extract often uncertain raw data. For example, the systematic gathering of the prices of products sold online over a period of time. Secondly, "The Online digestion of semi structured data and unstructured data", which includes news articles, reviews of services and products and opinion polls on social media that aid in the determination of public perception, trends and contemporary events that are generating interest across the globe. Thirdly, a 'Real-time Correlation of streaming data with slowly accessible historical data repositories,' which refers to the "mechanisms used for correlating and integrating data in real-time with historical records."[24] The purpose of this stage is to derive a contextualized perception of personalized information that seeks to add value to the data by providing a historical context to it. Big Data for development purposes would make use of a combination of these depending on the context and need.

(i) Policy Formulation

The world today has become increasingly volatile in terms of how the decisions of certain countries are beginning to have an impact on vulnerable communities within entirely different nations. Our global economy has become infinitely more susceptible to fluctuating conditions primarily because of its interconnectivity hinged upon transnational interdependence. The primordial instigators of most of these changes, including the nature of harvests, prices of essential commodities, employment structures and capital flows, have been financial and environmental disruptions. [25] According to the OECD, " Disruptive shocks to the global economy are likely to become more frequent and cause greater economic and social hardship. The economic spillover effects of events like the financial crisis or a potential pandemic will grow due to the increasing interconnectivity of the global economy and the speed with which people, goods and data travel."[26]

The local impacts of these fluctuations may not be easily visible or even traceable but could very well be severe and long lasting. A vibrant literature on the vulnerability of communities has highlighted the impacts of these shocks on communities often causing children to drop out of school, families to sell their productive assets, and communities to place a greater reliance on state rations.[27] These vulnerabilities cannot be definitively discerned through traditional systems of monitoring and information collection. The evidence of the effects of these shocks often take too long to reach decision makers; who are unable to formulate effective policies without ascertaining the nature and extent of the hardships suffered by these in a given context. The existing early warning systems in place do help raise flags and draw attention to the problem but their reach is limited and veracity compromised due to the time it takes to extract and collate this information through traditional means. These traditional systems of information collection are difficult to implement within rural impoverished areas and the data collected is not always reliable due to the significant time gap in its collection and subsequent interpretation. Data collected from surveys does provide an insight into the state of affairs of communities across demographics but this requires time to be collected, processed, verified and eventually published. Further, the expenses incurred in this process often prove to be difficult to offset.

The digital revolution therefore provides a significant opportunity to gain a richer and deeper insight into the very nature and evolution of the human experience itself thus affording a more legitimate platform upon which policy deliberations can be articulated. This data driven decision making, once the monopoly of private institutions such as The World Economic Forum and The McKinsey Institute [28] has now emerged at the forefront of the public policy discourse. Civil society has also expressed an eagerness to be more actively involved in the collection of real-time data after having perceived its benefits. This is evidenced by the emergence of 'crowd sourcing'[29] and other 'participatory sensing' [30] efforts that are founded upon the commonalities shared by like minded communities of individuals. This is being done on easily accessible platforms such as mobile phone interfaces, hand-held radio devices and geospatial technologies. [31]

The predictive nature of patterns identifiable from big data is extremely relevant for the purpose of developing socio-economic policies that seek to bridge problem-solution gaps and create a conducive environment for growth and development. Mobile phone technology has been able to quantify human behavior on an unprecedented scale.[32] This includes being able to detect changes in standard commuting patterns of individuals based on their employment status[33] and estimating a country's GDP in real-time by measuring the nature and extent of light emissions through remote sensing. [34]

A recent research study has concluded that "due to the relative frequency of certain queries being highly correlated with the percentage of physician visits in which individuals present influenza symptoms, it has been possible to accurately estimate the levels of influenza activity in each region of the United States, with a reporting lag of just a day." Online data has thus been used as a part of syndromic surveillance efforts also known as infodemiology. [35] The US Centre for Disease Control has concluded that mining vast quantities of data through online health related queries can help detect disease outbreaks " before they have been confirmed through a diagnosis or a laboratory confirmation." [36] Google trends works in a similar way.

Another public health monitoring system known as the Healthmap project compiles seemingly fragmented data from news articles, social media, eye-witness reports and expert discussions based on validated studies to "achieve a unified and comprehensive view of the current global state of infectious diseases" that may be visualized on a map. [37]

Big Data used for development purpose can reduce the reliance on human inputs thus narrowing the room for error and ensuring the accuracy of information collected upon which policy makers can base their decisions.

(ii) Advocacy and Social Change

Due to the ability of Big Data to provide an unprecedented depth of detail on particular issues, it has often been used as a vehicle of advocacy to highlight various issues in great detail. This makes it possible to ensure that citizens are provided with a far more participative experience, capturing their attention and hence better communicating these problems. Numerous websites have been able to use this method of crowd sourcing to broadcast socially relevant issues[38]. Moreover, the massive increase in access to the internet has dramatically improved the scope for activism through the use of volunteered data due to which advocates can now collect data from volunteers more effectively and present these issues in various forums. Websites like Ushahidi[39] and the Black Monday Movement [40] being prime examples of the same. These platforms have championed various causes, consistently exposing significant social crises' that would otherwise go unnoticed.

The Ushahidi application used crowd sourcing mechanisms in the aftermath of the Haiti earthquake to set up a centralized messaging system that allowed mobile phone users to provide information on injured and trapped people.[41] An analysis of the data showed that the concentration of text messages was correlated with the areas where there was an increased concentration of damaged buildings. [42] Patrick Meier of Ushahidi noted "These results were evidence of the system's ability to predict, with surprising accuracy and statistical significance, the location and extent of structural damage post the earthquake." [43]

Another problem that data advocacy hopes to tackle, however, is that of too much exposure, with advocates providing information to various parties to help ensure that there exists no unwarranted digital surveillance and that sensitive advocacy tools and information are not used inappropriately. An interesting illustration of the same is The Tactical Technology Collective[44] that hopes to improve the use of technology by activists and various other political actors. The organization, through various mediums such as films, events etc. hopes to train activists regarding data protection and privacy awareness and skills among human rights activists. Additionally, Tactical Technology also assists in ensuring that information is used in an appealing and relevant manner by human rights activists and in the field of capacity building for the purposes of data advocacy.

Observed data such as mobile phone records generated through network operators as well as through the use of social media are beginning to embody an omnipotent role in the development of academia through detailed research. This is due to the ability of this data to provide microcosms of information within both contexts of finer granularity and over larger public spaces. In the wake of natural disasters, this can be extremely useful, as reflected by the work of Flowminder after the 2010 Haiti earthquake.[45] A similar string of interpretive analysis can be carried out in instances of conflict and crises over varying spans of time. Flowminder used the geospatial locations of 1.9 million subscriber identity modules in Haiti, beginning 42 days before the earthquake and 158 days after it. This information allowed researches to empirically determine the migration patterns of population post the earthquake and enabled a subsequent UNFPA household survey.[46] In a similar capacity, the UN Global Pulse is seeking to assist in the process of consultation and deliberation on the specific targets of the millennium development goals through a framework of visual analytics that represent the big data procured on each of the topics proposed for the post- 2015 agenda online.[47]

A recent announcement of collaboration between RTI International, a non-profit research organization and IBM research lab looks promising in its initiative to utilize big data analytics in schools within Mombasa County, Kenya.[48] The partnership seeks to develop testing systems that would capture data that would assist governments, non-profit organizations and private enterprises in making more informed decisions regarding the development of education and human resources within the region. Äs observed by Dr. Kamal Bhattacharya, The Vice President of IBM Research, "A significant lack of data on Africa in the past has led to misunderstandings regarding the history, economic performance and potential of the government." The project seeks to improve transparency and accountability within the schooling system in more than 100 institutions across the county. The teachers would be equipped with tablet devices to collate the data about students, classrooms and resources. This would allow an analysis of the correlation between the three aspects thus enabling better policy formulation and a more focused approach to bettering the school system. [49] This is a part of the United States Agency for International Development's Education Data for Decision Making (EdData II) project. According to Dr Kommy Weldemariam, Research Scientist , IBM Research, "… there has been a significant struggle in making informed decisions as to how to invest in and improve the quality and content of education within Sub-Saharan Africa. The Project would create a school census hub which would enable the collection of accurate data regarding performance, attendance and resources at schools. This would provide valuable insight into the building of childhood development programs that would significantly impact the development of an efficient human capital pool in the near future."[50]

A similar initiative has been undertaken by Apple and IBM in the development of the "Student Achievement App" which seeks to use this data for "content analysis of student learning". The Application as a teaching tool that analyses the data provided to develop actionable intelligence on a per-student basis." [51] This would give educators a deeper understanding of the outcome of teaching methodologies and subsequently enable better leaning. The impact of this would be a significant restructuring of how education is delivered. At a recent IBM sponsored workshop on education held in India last year , Katharine Frase, IBM CTO of Public Sector predicted that "classrooms will look significantly different within a decade than they have looked over the last 200 years."[52]

(iii) Access and the exchange of information

Big data used for development serves as an important information intermediary that allows for the creation of a unified space within which unstructured heterogeneous data can be efficiently organized to create a collaborative system of information. New interactive platforms enable the process of information exchange though an internal vetting and curation that ensures accessibility to reliable and accurate information. This encourages active citizen participation in the articulation of demands from the government, thus enabling the actualization of the role of the electorate in determining specific policy decisions.

The Grameen Foundation's AppLab in Kampala aids in the development of tools that can use the information from micro financing transactions of clients to identify financial plans and instruments that would be be more suitable to their needs.[53] Thus, through working within a community, this technology connects its clients in a web of information sharing that they both contribute to and access after the source of the information has been made anonymous. This allows the individual members of the community to benefit from this common pool of knowledge. The AppLab was able to identify the emergence of a new crop pest from an increase in online searches for an unusual string of search terms within a particular region. Using this as an early warning signal, the Grameen bank sent extension officers to the location to check the crops and the pest contamination was dealt with effectively before it could spread any further.[54]

(iv) Accountability and Transparency

Big data enables participatory contributions from the electorate in existing functions such as budgeting and communication thus enabling connections between the citizens, the power brokers and elites. The extraction of information and increasing transparency around data networks is also integral to building a self-sustaining system of data collection and analysis. However it is important to note that this information collected must be duly analyzed in a responsible manner. Checking the veracity of the information collected and facilitating individual accountability would encourage more enthusiastic responses from the general populous thus creating a conducive environment to elicit the requisite information. The effectiveness of the policies formulated by relying on this information would rest on the accuracy of such information.

An example of this is Chequeado, a non-profit Argentinean media outlet that specializes in fact-checking. It works on a model of crowd sourcing information on the basis of which it has fact checked everything from the live presidential speech to congressional debates that have been made open to the public. [55] It established a user friendly public database, DatoCHQ, in 2014 which allowed its followers to participate in live fact-checks by sending in data, which included references, facts, articles and questions, through twitter. [56] This allowed citizens to corroborate the promises made by their leaders and instilled a sense of trust in the government.

III. Big Data and Smart Cities in the Global South

Smart cities have become a buzzword in South Asia, especially after the Indian government led by Prime Minister Narendra Modi made a commitment to build 100 smart cities in India[57]. A smart city is essentially designed as a hub where the information and communication technologies (ICT) are used to create feedback loops with an almost minimum time gap. In traditional contexts, surveys carried out through a state sponsored census were the only source of systematic data collection. However these surveys are long drawn out processes that often result in a drain on State resources. Additionally, the information obtained is not always accurate and policy makers are often hesitant to base their decisions on this information. The collection of data can however be extremely useful in improving the functionality of the city in terms of both the 'hard' or physical aspects of the infrastructural environment as well as the 'soft' services it provides to citizens. One model of enabling this data collection, to this effect, is a centrally structured framework of sensors that may be able to determine movements and behaviors in real-time, from which the data obtained can be subsequently analyzed. For example, sensors placed under parking spaces at intersections can relay such information in short spans of time. South Korea has managed to implement a similar structure within its smart city, Songdo.[58]

Another approach to this smart city model is using crowd sourced information through apps, either developed by volunteers or private conglomerates. These allow for the resolving of specific problems by organizing raw data into sets of information that are attuned to the needs of the public in a cohesive manner. However, this system would require a highly structured format of data sets, without which significantly transformational result would be difficult to achieve.[59]

There does however exist a middle ground, which allows the beneficiaries of this network, the citizens, to take on the role of primary sensors of information. This method is both cost effective and allows for an experimentation process within which an appropriate measure of the success or failure of the model would be discernible in a timely manner. It is especially relevant in fast growing cities that suffer congestion and breakdown of infrastructure due to the unprecedented population growth. This population is now afforded with the opportunity to become a part of the solution.

The principle challenge associated with extracting this Big Data is its restricted access. Most organizations that are able to collect this big data efficiently are private conglomerates and business enterprises, who use this data to give themselves a competitive edge in the market, by being able to efficiently identify the needs and wants of their clientele. These organizations are reluctant to release information and statistics because they fear it would result in them losing their competitive edge and they would consequently lose the opportunity to benefit monetarily from the data collected. Data leaks would also result in the company getting a bad name and its reputation could be significantly hampered. Despite the individual anonymity, the transaction costs incurred in ensuring the data of their individual customers is protected is often an expensive process. In addition to this there is a definite human capital gap resulting from the significant lack of scientists and analysts to interpret raw data transmitted across various channels.

(i) Big Data in Urban Planning

Urban planning would require data that is reflective of the land use patterns of communities, combined with their travel descriptions and housing preferences. The mobility of individuals is dependent on their economic conditions and can be determined through an analysis of their purchases, either via online transactions or from the data accumulated by prominent stores. The primary source of this data is however mobile phones, which seemed to have transcend economic barriers. Secondary sources include cards used on public transport such as the Oyster card in London and the similar Octopus card used in Hong Kong. However, in most developing countries these cards are not available for public transport systems and therefore mobile network data forms the backbone of data analytics. An excessive reliance on the data collected through Smart phones could however be detrimental, especially in developing countries, simply because the usage itself would most likely be concentrated amongst more economically stable demographics and the findings from this data could potentially marginalize the poor.[60]

Mobile network big data (MNBD) is generated by all phones and includes CDRs, which are obtained from calls or texts that are sent or received, internet usage, topping up a prepaid value and VLR or Visitor Location Registry data which is generated whenever the phone is question has power. It essentially communicates to the Base Transceiver Stations (BSTs) that the phone is in the coverage area. The CDR includes records of calls made, duration of the call and information about the device. It is therefore stored for a longer period of time. The VLR data is however larger in volume and can be written over. Both VLR and CDR data can provide invaluable information that can be used for urban planning strategies. [61] LIRNEasia, a regional policy and regulation think-tank has carried out an extensive study demonstrating the value of MNBD in SriLanka.[62] This has been used to understand and sometimes even monitor land use patterns, travel patterns during peak and off seasons and the congregation of communities across regions. This study was however only undertaken after the data had been suitably pseudonymised.[63] The study revealed that MNBD was incredibly valuable in generating important information that could be used by policy formulators and decision makers, because of two primary characteristics. Firstly, it comes close to a comprehensive coverage of the demographic within developing countries, thus using mobile phones as sensors to generate useful data. Secondly, people using mobile phones across vast geographic areas reflect important information regarding patterns of their travel and movement. [64]

MNBD allows for the tracking and mapping of changes in population densities on a daily basis, thus identifying 'home' and 'work' locations, informing policy makers of population congestion so that thy may be able to formulate policies with respect to easing this congestion. According to Rohan Samarajiva, founding chair of LIRNEasia, "This allows for real-time insights on the geo-spatial distribution of population, which may be used by urban planners to create more efficient traffic management systems."^{^[65]} This can also be used for the developmental economic policies. For example, the northern region of Colombo, a region inhabited by the low income families shows a lower population density on weekdays. This is reflective of the large numbers travelling to southern Colombo for employment. ^{^[66]}Similarly, patterns of land use can be ascertained by analyzing the various loading patterns of base stations. Building on the success of the Mobile Data analysis project in SriLanka LIRNEasia plans to collaborate with partners in India and Bangladesh to assimilate real time information about the behavioral tendencies of citizens, using which policy makers may be able to make informed decisions. When this data is combined with user friendly virtual platforms such as smartphone Apps or web portals, it can also help citizens make informed choices about their day to day activities and potentially beneficial long term decisions. ^{^[67]}

Challenges of using Mobile Network Data

Mobile networks invest significant sums of money in obtaining information regarding usage patterns of their services. Consequently, they may use this data to develop location based advertizing. In this context, there is a greater reluctance to share data for public purposes. Allowing access to one operator's big data by another could result in significant implications on the other with respect to the competitive advantage shared by the operator. A plausible solution to this conundrum is the accumulation of data from multiple sources without separating or organizing it according to the source it originates from. There is thus a lesser chance of sensitive information of one company being used by another. However, even operators do have concerns about how the data would be handled before this "mashing up" occurs and whether it might be leaked by the research organization itself. LIRNEasia used comprehensive non-disclosure agreements to ensure that the researchers who worked with the data were aware of the substantial financial penalties that may be imposed on them for data breaches. The access to the data was also restricted. [68]

Another line of argumentation advocates for the open sharing of data. A recent article in the Economist has articulated this in the context of the Ebola outbreak in West Africa. " Releasing the data, though, is not just a matter for firms since people's privacy is involved. It requires governmental action as well. Regulators in each affected country would have to order operators to make their records accessible to selected researchers, who through legal agreements would only be allowed to use the data in a specific manner. For example, Orange, a major mobile phone network operator has made millions of CDRs from Senegal and The Ivory Coast available for researchers for their use under its Data Development Initiative. However the Political will amongst regulators and Network operators to do this seems to be lacking."[69]

It would therefore be beneficial for companies to collaborate with the customers who create the data and the researchers who want to use it to extract important insights. This however would require the creation of and subsequent adherence to self regulatory codes of conduct. [70] In addition to this cooperation between network operators will assist in facilitating the transference of the data of their customers to research organizations. Sri Lanka is an outstanding example of this model of cooperation which has enabled various operators across spectrums to participate in the mobile-money enterprise.[71]

(ii) Big Data and Government Delivery of Services and Functions

The analysis of Data procured in real time has proven to be integral to the formulation of policies, plans and executive decisions. Especially in an Asian context, Big data can be instrumental in urban development, planning and the allocation of resources in a manner that allows the government to keep up with the rapidly growing demands of an empowered population whose numbers are on an exponential rise. Researchers have been able to use data from mobile networks to engage in effective planning and management of infrastructure, services and resources. If, for example, a particular road or highway has been blocked for a particular period of time an alternative route is established before traffic can begin to build up creating a congestion, simply through an analysis of information collected from traffic lights, mobile networks and GPS systems.[72]

There is also an emerging trend of using big data for state controlled services such as the military. The South Korean Defense Minister Han Min Koo, in his recent briefing to President Park Geun-hye reflected on the importance of innovative technologies such as Big Data solutions. [73]

The Chinese government has expressed concerns regarding data breaches and information leakages that would be extremely dangerous given the exceeding reliance of governments on big data. A security report undertaken by Qihoo 360, China's largest software security provider established that 2,424 of the 17,875 Web security loopholes were on government websites. Considering the blurring line between government websites and external networks, it has become all the more essential for authorities to boost their cyber security protections.[74]

The Japanese government has considered investing resources in training more data scientists who may be able to analyze the raw data obtained from various sources and utilize requisite techniques to develop an accurate analysis. The Internal Affairs and Communication Ministry planned to launch a free online course on big data, the target of which would be corporate workers as well as government officials.[75]

Data analytics is emerging as an efficient technique of monitoring the public transport management systems within Singapore. A recent collaboration between IBM, StarHub, The Land Transport Authority and SMRT initiated a research study to observe the movement of commuters across regions. ^{^[76]} This has been instrumental in revamping the data collection systems already in place and has allowed for the procurement of additional systems of monitoring.^{^[77]} The idea is essentially to institute a "black box" of information for every operational unit that allows for the relaying of real-time information from sources as varied as power switches, tunnel sensors and the wheels, through assessing patterns of noise and vibration. ^{^[78]}

In addition to this there are numerous projects in place that seek to utilize Big Data to improve city life. According to Carlo Ritti, Director of the MIT Senseable City Lab, "We are now able to analyze the pulse of a city from moment to moment. Over the past decade, digital technologies have begun to blanket our cities, forming the backbone of a large, intelligent infrastructure." ^{^[79]} The professor of Information Architecture and Founding Director of the Singapore ETH Centre, Gerhart Schmitt has observed that "the local weather has a major impact on the behavior of a population." In this respect the centre is engaged in developing a range of visual platforms to inform citizens on factors such as air quality which would enable individuals to make everyday choices such as what route to take when planning a walk or predict a traffic jam. ^{^[80]} Schmitt's team has also been able to arrive at a pattern that connects the demand for taxis with the city's climate. The amalgamation of taxi location with rainfall data has been able to help locals hail taxis during a storm. This form of data can be used in multiple ways allowing the visualization of temperature hotspots based on a "heat island" effect where buildings, cars and cooling units cause a rise in temperature. ^{^[81]}

Microsoft has recently entered into a partnership with the Federal University of Minas Gerais, one of the largest universities in Brazil to undertake a research project that could potentially predict traffic jams up to an hour in advance. ^{^[82]} The project attempts to analyze information from transport departments, road traffic cameras and drivers social network profiles to identify patterns that they could use to help predict traffic jams approximately 15 to 60 minutes before they actually happen.^{^[83]}

In anticipation of the increasing demand for professionals with requisite training in data sciences, the Malaysian Government has planned to increase the number of local data scientists from the present 80 to 1500 by 2020, through the support of the universities within the country.

IV. Big Data and the Private Sector in the Global South

Essential considerations in the operations of Big Data in the Private sector in the Asia Pacific region have been extracted by a comprehensive survey carried out by the Economist Intelligence Unit.[84] Over 500 executives across the Asia Pacific region were surveyed, from across industries representing a diverse range of functions. 69% of these companies had an annual turnover of over US $500m. The respondents were senior managers responsible for taking key decisions with regard to investment strategies and the utilization of big data for the same.

The results of the Survey conclusively determined that firms in the Asia Pacific region have had limited success with implementing Big Data Practices. A third of the respondents claimed to have an advanced knowledge of the utilization of big data while more than half claim to have made limited progress in this regard. Only 9% of the Firms surveyed cited internal barriers to implementing big data practices. This included a significant difficulty in enabling the sharing of information across boundaries. Approximately 40% of the respondents surveyed claimed they were unaware of big data strategies, even if they had in fact been in place simply because these had been poorly communicated to them. Almost half of the firms however believed that big data plays an important role in the success of the firm and that it can contribute to increasing revenue by 25% or more.

Numerous obstacles in the adoption of big data were cited by the respondents. These include the lack of suitable software to interpret the data and the lack of in-house skills to analyze the data appropriately. In addition to this, the lack of willingness on the part of various departments to share their data for the fear of a breach or leak was thought to be a major hindrance. This combined with a lack of communication between the various departments and exceedingly complicated reports that cannot be analyzed given the limited resources and lack of human capital qualified enough to carry out such an analysis, has resulted in an indefinite postponement of any policy propounding the adoption of big data practices.

Over 59% of the firms surveyed agreed that collaboration is integral to innovation and that information silos are a huge hindrance within a knowledge based economy. There is also a direct correlation between the size of the company and its progress in adopting big data, with larger firms adopting comprehensive strategies more frequently than smaller ones. A major reason for this is that large firms with substantially greater resources are able to actualize the benefits of big data analytics more efficiently than firms with smaller revenues. These businesses which have advanced policies in place outlining their strategies with respect to their reliance on big data are also more likely to communicate these strategies to their employees to ensure greater clarity in the process.

The use of big data was recently voted as the "best management practice" of the past year according to a cumulative ranking published by Chief Executive China Magazine, a Trade journal published by Global Sources on 13th January, 2015 in Beijing. The major benefit cited was the real-time information sourced from customers, which allows for direct feedback from clients when making decisions regarding changes in products or services. [85]

A significant contributor to the lack of adequate usage of data analytics is the belief that a PhD is a prerequisite for entering the field of data science. This misconception was pointed out by Richard Jones, vice president of Cloudera in the Australia, New Zealand and the Asean region. Cloudera provides businesses with the requisite professional services that they may need to effectively utilize Big Data. This includes a combination of the necessary manpower, technology and consultancy services.^{^[86]} Deepak Ramanathan, the chief technology officer, SAS Asia Pacific believes that this skill gap can be addressed by forming data science teams within both governments and private enterprises. These teams could comprise of members with statistical, coding and business skills and allow them to work in a collaborative manner to address the problem at hand.^{^[87]} SAS is an Enterprise Software Giant that creates tools tailored to suit business users to help them interpret big data. Eddie Toh, the planning and marketing manager of Intel's data center platform believes that businesses do not necessarily need data scientists to be able to use big data analytics to their benefit and can in fact outsource the technical aspects of the interpretation of this data as and when required.^{^[88]}

The analytical team at Dell has forged a partnership with Brazilian Public Universities to facilitate the development of a local talent pool in the field of data analytics. The Instituto of Data Science (IDS) will provide training methodologies for in person or web based classes. ^{^[89]} The project is being undertaken by StatSoft, a subsidiary of Dell that was acquired by the technology giant last year. ^{^[90]}

V. Conclusion

There have emerged numerous challenges in the analysis and interpretation of Big Data. While it presents an extremely engaging opportunity, which has the potential to transform the lives of millions of individuals, inform the private sector and influence government, the actualization of this potential requires the creation of a sustainable foundational framework ; one that is able to mitigate the various challenges that present themselves in this context.

A colossal increase in the rate of digitization has resulted in an unprecedented increment in the amount of Big Data available, especially through the rapid diffusion cellular technology. The importance of mobile phones as a significant source of data, especially in low income demographics cannot be overstated. This can be used to understand the needs and behaviors of large populations, providing an in depth insight into the relevant context within which valuable assessments as to the competencies, suitability and feasibilities of various policy mechanisms and legal instruments can be made. However, this explosion of data does have a lasting impact on how individuals and organizations interact with each other, which might not always be reflected in the interpretation of raw data without a contextual understanding of the demographic. It is therefore vital to employ the appropriate expertise in assessing and interpreting this data. The significant lack of a human resource to capital to analyze this information in an accurate manner poses a definite challenge to its effective utilization in the Global South.

The legal and technological implications of using Big Data are best conceptualized within the deliberations on protecting the privacy of the contributors to this data. The primary producers of this information, from across platforms, are often unaware that they are in fact consenting to the subsequent use of the data for purposes other than what was intended. For example people routinely accept terms and conditions of popular applications without understanding where or how the data that they inadvertently provide will be used.[91] This is especially true of media generated on social networks that are increasingly being made available on more accessible platforms such as mobile phones and tablets. Privacy has and always will remain an integral pillar of democracy. It is therefore essential that policy makers and legislators respond effectively to possible compromises of privacy in the collection and interpretation of this data through the institution of adequate safeguards in this respect.

Another challenge that has emerged is the access and sharing of this data. Private corporations have been reluctant to share this data due to concerns about potential competitors being able to access and utilize the same. In addition to this, legal considerations also prevent the sharing of data collected from their customers or users of their services. The various technical challenges in storing and interpreting this data adequately also prove to be significant impediments in the collection of data. It is therefore important that adequate legal agreements be formulated in order to facilitate a reliable access to streams of data as well as access to data storage facilities to accommodate for retrospective analysis and interpretation.

In order for the use of Big Data to gain traction, it is important that these challenges are addressed in an efficient manner with durable and self-sustaining mechanisms of resolving significant obstructions. The debates and deliberations shaping the articulation of privacy concerns and access to such data must be supported with adequate tools and mechanisms to ensure a system of "privacy-preserving analysis." The UN Global Pulse has put forth the concept of data philanthropy to attempt to resolve these issues, wherein " corporations [would] take the initiative to anonymize (strip out all personal information) their data sets and provide this data to social innovators to mine the data for insights, patterns and trends in realtime or near realtime."[92]

The concept of data philanthropy highlights particular challenges and avenues that may be considered for future deliberations that may result in specific refinements to the process.

One of the primary uses of Big Data, especially in developing countries is to address important developmental issues such as the availability of clean water, food security, human health and the conservation of natural resources. Effective Disaster management has also emerged as one of the key functions of Big Data. It therefore becomes all the more important for organizations to assess the information supply chains pertaining to specific data sources in order to identify and prioritize the issues of data management. [93] Data emerging from different contexts, across different sources may appear in varied compositions and would differ significantly across economic demographics. The Big Data generated from certain contexts would be inefficient due to the unavailability of data within certain regions and the resulting studies affecting policy decisions should take into account this discrepancy. This data unavailability has resulted in a digital divide which is especially prevalent in the global south. [94]

Appropriate analysis of the Big Data generated would provide a valuable insight into the key areas and inform policy makers with respect to important decisions. However, it is necessary to ensure that the quality of this data meets a specific standard and appropriate methodological processes have been undertaken to interpret and analyze this data. The government is a key actor that can shape the ecosystem surrounding the generation, analysis and interpretation of big data. It is therefore essential that governments of countries across the global south recognize the need to collaborate with civic organizations as well technical experts in order to create appropriate legal frameworks for the effective utilization of this data.

[1] Onella, Jukka- Pekka. "Social Networks and Collective Human Behavior." UN Global Pulse. 10 Nov.2011. <http://www.unglobalpulse.org/node/14539>

[2] http://www.business2community.com/big-data/evaluating-big-data-predictive-analytics-01277835

[3] Ibid

[4] http://unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf

[5] Ibid, p.13, pp.5

[6] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[7] Helbing, Dirk , and Stefano Balietti. "From Social Data Mining to Forecasting Socio-Economic Crises." Arxiv (2011) 1-66. 26 Jul 2011 http://arxiv.org/pdf/1012.0178v5.pdf.

[8] Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh andAngela H. Byers. "Big data: The next frontier for innovation, competition, and productivity." McKinsey

Global Institute (2011): 1-137. May 2011.

[9] "World Population Prospects, the 2010 Revision." United Nations Development Programme. <http://esa.un.org/unpd/wpp/unpp/panel_population.htm>

[10] Mobile phone penetration, measured by Google, from the number of mobile phones per 100 habitants, was 96% in Botswana, 63% in Ghana, 66% in Mauritania, 49% in Kenya, 47% in Nigeria, 44% in Angola, 40% in Tanzania (Source: Google Fusion Tables)

[11] http://www.brookings.edu/blogs/africa-in-focus/posts/2015/04/23-big-data-mobile-phone-highway-sy

[12] Ibid

[13] <http://www.google.com/fusiontables/Home/>

[14] "Global Internet Usage by 2015 [Infographic]." Alltop. <http://holykaw.alltop.com/global-internetusage-by-2015-infographic?tu3=1>

[15] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011 <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[16] Ibid

[17] Ibid

[18] Ibid

[19] Goetz, Thomas. "Harnessing the Power of Feedback Loops." Wired.com. Conde Nast Digital, 19 June 2011. <http://www.wired.com/magazine/2011/06/ff_feedbackloop/all/1>.

[20] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[21] Bollier, David. The Promise and Peril of Big Data. The Aspen Institute, 2010. <http://www.aspeninstitute.org/publications/promise-peril-big-data>

[22] Ibid

[23] Eagle, Nathan and Alex (Sandy) Pentland. "Reality Mining: Sensing Complex Social Systems",Personal and Ubiquitous Computing, 10.4 (2006): 255-268.

[24] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[25] OECD, Future Global Shocks, Improving Risk Governance, 2011

[26] "Economy: Global Shocks to Become More Frequent, Says OECD." Organisation for Economic Cooperationand Development. 27 June. 2011.

[27] Friedman, Jed, and Norbert Schady. How Many More Infants Are Likely to Die in Africa as a Result of the Global Financial Crisis? Rep. The World Bank <http://siteresources.worldbank.org/INTAFRICA/Resources/AfricaIMR_FriedmanSchady_060209.pdf>

[28] Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute,June 2011<http://www.mckinsey.com/mgi/publications/big_data/pdfs/MGI_big_data_full_report.pdf>

[29] The word "crowdsourcing" refers to the use of non-official actors ("the crowd") as (free) sources of information, knowledge and services, in reference and opposition to the commercial practice of

outsourcing. "

[30] Burke, J., D. Estrin, M. Hansen, A. Parker, N. Ramanthan, S. Reddy and M.B. Srivastava. ParticipatorySensing. Rep. Escholarship, University of California, 2006. <http://escholarship.org/uc/item/19h777qd>.

[31] "Crisis Mappers Net-The international Network of Crisis Mappers." <http://crisismappers.net>, http://haiti.ushahidi.com and Goldman et al., 2009

[32] Alex Pentland cited in "When There's No Such Thing As Too Much Information". The New York Times.23 Apr. 2011<http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.

[33] Nathan Eagle also cited in "When There's No Such Thing As Too Much Information". The New YorkTimes. 23 Apr. 2011. <http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.

[34] Helbing and Balietti. "From Social Data Mining to Forecasting Socio-Economic Crisis."

[35] Eysenbach G. Infodemiology: tracking flu-related searches on the Web for syndromic surveillance.AMIA (2006)<http://yi.com/home/EysenbachGunther/publications/2006/eysenbach2006cinfodemiologyamia proc.pdf>

[36] Syndromic Surveillance (SS)." Centers for Disease Control and Prevention. 06 Mar. 2012.<http://www.cdc.gov/ehrmeaningfuluse/Syndromic.html>.

[37] Health Map <http://healthmap.org/en/>

[38] see www.detective.io

[39] www.ushahidi.com

[40] www.facebook.com/BlackMondayMovement

[41] Ushahidi is a nonprofit tech company that was developed to map reports of violence in Kenya followingthe 2007 post-election fallout. Ushahidi specializes in developing "free and open source software for

information collection, visualization and interactive mapping." <http://ushahidi.com>

[42] Conducted by the European Commission's Joint Research Center against data on damaged buildingscollected by the World Bank and the UN from satellite images through spatial statistical techniques.

[43] www.ushahidi.com

[44] See https://tacticaltech.org/

[45] see www. flowminder.org

[46] Ibid

[47] http://post2015.unglobalpulse.net/

[48] http://allafrica.com/stories/201507151726.html

[49] Ibid

[50] Ibid

[51] http://www.computerworld.com/article/2948226/big-data/opinion-apple-and-ibm-have-big-data-plans-for-education.html

[52] Ibid

[53] http://www.grameenfoundation.org/where-we-work/sub-saharan-africa/uganda

[54] Ibid

[55] http://chequeado.com/

[56] http://datochq.chequeado.com/

[57] Times of India (2015): "Chandigarh May Become India's First Smart City," 12 January, http://timesofi ndia.indiatimes.com/india/Chandigarh- may-become-Indias-fi rst-smart-city/articleshow/ 45857738.cms

[58] http://www.cisco.com/web/strategy/docs/scc/ioe_citizen_svcs_white_paper_idc_2013.pdf

[59] Townsend, Anthony M (2013): Smart Cities: Big Data, Civic Hackers and the Quest for a New Utopia, New York: WW Norton.

[60] See "Street Bump: Help Improve Your Streets" on Boston's mobile app to collect data on roadconditions, http://www.cityofboston.gov/DoIT/ apps/streetbump.asp

[61] Mayer-Schonberger, V and K Cukier (2013): Big Data: A Revolution That Will Transform How We Live, Work, and Think, London: John Murray.

[62] http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html

[63] Ibid

[64] Newman, M E J and M Girvan (2004): "Finding and Evaluating Community Structure in Networks,"Physical Review E, American Physical Society, Vol 69, No 2.

[65] http://www.sundaytimes.lk/150412/sunday-times-2/big-data-can-make-south-asian-cities-smarter-144237.html

[66] Ibid

[67] Ibid

[68] http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html

[69] GSMA (2014): "GSMA Guidelines on Use of Mobile Data for Responding to Ebola," October, http:// www.gsma.com/mobilefordevelopment/wpcontent/ uploads/2014/11/GSMA-Guidelineson-

protecting-privacy-in-the-use-of-mobilephone- data-for-responding-to-the-Ebola-outbreak-_ October-2014.pdf

[70] An example of the early-stage development of a self-regulatory code may be found at http:// lirneasia.net/2014/08/what-does-big-data-sayabout- sri-lanka/

[71] See "Sri Lanka's Mobile Money Collaboration Recognized at MWC 2015," http://lirneasia. net/2015/03/sri-lankas-mobile-money-colloboration- recognized-at-mwc-2015/

[72] http://www.thedailystar.net/big-data-for-urban-planning-57593

[73] http://koreaherald.com , 19/01/2015

[74] http://www.news.cn/, 25/11/2014

[75] http://the-japan-news.com , 20/01/2015

[76] http://www.todayonline.com/singapore/can-big-data-help-tackle-mrt-woes

[77] Ibid

[78] Ibid

[79] http://edition.cnn.com/2015/06/24/tech/big-data-urban-life-singapore/

[80] Ibid

[81] Ibid

[82] http://venturebeat.com/2015/04/03/how-microsofts-using-big-data-to-predict-traffic-jams-up-to-an-hour-in-advance/

[83] Ibid

[84] https://www.hds.com/assets/pdf/the-hype-and-the-hope-summary.pdf

[85] http://www.news.cn , 14/01/2015

[86] http://www.techgoondu.com/2015/06/29/plugging-the-big-data-skills-gap/

[87] Ibid

[88] Ibid

[89] http://www.zdnet.com/article/dell-to-create-big-data-skills-in-brazil/

[90] Ibid

[91] Efrati, Amir. "'Like' Button Follows Web Users." The Wall Street Journal. 18 May 2011.

<http://online.wsj.com/article/SB10001424052748704281504576329441432995616.html>

[92] Krikpatrick, Robert. "Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience."

UN Global Pulse. 16 Sept. 2011. <http://www.unglobalpulse.org/blog/data-philanthropy-public-privatesector-data-sharing-global-resilience>

[93] Laney D (2001) 3D data management: Controlling data volume, velocity and variety. Available at: http://blogs. gartner.com/doug-laney/files/2012/01/ad949-3D-DataManagement-Controlling-Data-Volume-Velocity-andVariety.pdf

[94] Boyd D and Crawford K (2012) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society 15(5): 662-679.

For more details visit https://cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis

Big Data in India: Benefits, Harms, and Human Rights - Workshop Report

Vidushi Marda, Akash Deep Singh and Geethanjali Jujjavarapu — 2016-11-18T12:58:19Z

The Centre for Internet and Society held a one-day workshop on “Big Data in India: Benefits, Harms and Human Rights” at India Habitat Centre, New Delhi on the 1st of October, 2016. This report is a compilation of the the issues discussed, ideas exchanged and challenges recognized during the workshop. The objective of the workshop was to discuss aspects of big data technologies in terms of harms, opportunities and human rights. The discussion was designed around an extensive study of current and potential future uses of big data for governance in India, that CIS has undertaken over the last year with support from the MacArthur Foundation.

Contents

Big Data: Definitions and Global South Perspectives

Aadhaar as Big Data

Seeding

Aadhaar and Data Security

Aadhaar’s Relational Arrangement with Big Data Scheme

The Myths surrounding Aadhaar

IndiaStack and FinTech Apps

Problems with UID

Big Data: Definitions and Global South Perspectives

“Big Data” has been defined by multiple scholars till date. The first consideration at the workshop was to discuss various definitions of big data, and also to understand what could be considered Big Data in terms of governance, especially in the absence of academic consensus. One of the most basic ways to define it, as given by the National Institute of Standards and Technology, USA, is to take it to be the data that is beyond the computational capacity of current systems. This definition has been accepted by the UIDAI of India. Another participant pointed out that Big Data is not only indicative of size, but rather the nature of data which is unstructured, and continuously flowing. The Gartner definition of Big Data relies on the three Vs i.e. Volume (size), Velocity (infinite number of ways in which data is being continuously collected) and Variety (the number of ways in which data can be collected in rows and columns).

The presentation also looked at ways in which Big Data is different from traditional data. It was pointed out that it can accommodate diverse unstructured datasets, and it is ‘relational’ i.e. it needs the presence of common field(s) across datasets which allows these fields to be conjoined. For e.g., the UID in India is being linked to many different datasets, and they don’t constitute Big Data separately, but do so together. An increasingly popular definition is to define data as “Big Data” based on what can be achieved through it. It has been described by authors as the ability to harness new kinds of insight which can inform decision making. It was pointed out that CIS does not subscribe to any particular definition, and is still in the process of coming up with a comprehensive definition of Big Data.

Further, discussion touched upon the approach to Big Data in the Global South. It was pointed out that most discussions about Big Data in the Global South are about the kind of value that it can have, the ways in which it can change our society. The Global North, on the other hand, has moved on to discussing the ethics and privacy issues associated with Big Data.

After this, the presentation focussed on case studies surrounding key Central Government initiatives and projects like Aadhaar, Predictive Policing, and Financial Technology (FinTech).

Aadhaar as Big Data

In presenting CIS’ case study on Aadhaar, it was pointed out that initially, Aadhaar, with its enrollment dataset was by itself being seen as Big Data. However, upon careful consideration in light of definitions discussed above, it can be seen as something that enables Big Data. The different e-governance projects within Digital India, along with Aadhaar, constitute Big Data. The case study discussed the Big Data implications of Aadhaar, and in particular looked at a ‘cradle to grave’ identity mapping through various e-government projects and the datafication of various transaction generated data.

Seeding

Any digital identity like Aadhaar typically has three features: 1. Identification i.e. a number or card used to identify yourself; 2. Authentication, which is based on your number or card and any other digital attributes that you might have; 3. Authorisation: As bearers of the digital identity, we can authorise the service providers to take some steps on our behalf. The case study discussed ‘seeding’ which enables the Big Data aspects of Digital India. In the process of seeding, different government databases can be seeded with the UID number using a platform called Ginger. Due to this, other databases can be connected to UIDAI, and through it, data from other databases can be queried by using your Aadhaar identity itself. This is an example of relationality, where fractured data is being brought together. At the moment, it is not clear whether this access by UIDAI means that an actual physical copy of such data from various sources will be transferred to UIDAI’s servers or if they will just access it through internet, but the data remains on the host government agency’s server. An example of even private parties becoming a part of this infrastructure was raised by a participant when it was pointed out that Reliance Jio is now asking for fingerprints. This can then be connected to the relational infrastructure being created by UIDAI. The discussion then focused on how such a structure will function, where it was mentioned that as of now, it cannot be said with certainty that UIDAI will be the agency managing this relational infrastructure in the long run, even though it is the one building it.

Aadhaar and Data Security

This case study also dealt with the sheer lack of data protection legislation in India except for S.43A of the IT Act. The section does not provide adequate protection as the constitutionality of the rules and regulations under S.43A is ambivalent. More importantly, it only refers to private bodies. Hence, any seeding which is being done by the government is outside the scope of data protection legislation. Thus, at the moment, no legal framework covers the processes and the structures being used for datasets. Due to the inapplicability of S.43A to public bodies, questions were raised as to the existence of a comprehensive data protection policy for government institutions. Participants answered the question in the negative. They pointed out that if any government department starts collecting data, they develop their own privacy policy. There are no set guidelines for such policies and they do not address concerns related to consent, data minimisation and purpose limitation at all. Questions were also raised about the access and control over Big Data with government institutions. A tentative answer from a participant was that such data will remain under the control of the domain specific government ministry or department, for e.g. MNREGA data with the Ministry of Rural Development, because the focus is not on data centralisation but rather on data linking. As long as such fractured data is linked and there is an agency that is responsible to link them, this data can be brought together. Such data is primarily for government agencies. But the government is opening up certain aspects of the data present with it for public consumption for research and entrepreneurial purposes.The UIDAI provides you access to your own data after paying a minimal fee. The procedure for such access is still developing.

Aadhaar’s Relational Arrangement with Big Data Scheme

The various Digital India schemes brought in by the government were elucidated during the workshop. It was pointed out that these schemes extend to myriad aspects of a citizen’s daily life and cover all the essential public services like health, education etc. This makes Aadhaar imperative even though the Supreme Court has observed that it is not mandatory for every citizen to have a unique identity number. The benefits of such identity mapping and the ecosystem being generated by it was also enumerated during the discourse. But the complete absence of any data ethics or data confidentiality principles make us unaware of the costs at which these benefits are being conferred on us. Apart from surveillance concerns, the knowledge gap being created between the citizens and the government was also flagged. Three main benefits touted to be provided by Aadhaar were then analysed. The first is the efficient delivery of services. This appears to be an overblown claim as the Aadhaar specific digitisation and automation does not affect the way in which employment will be provided to citizens through MNREGA or how wage payment delays will be overcome. These are administrative problems that Aadhaar and associated technologies cannot solve. The second is convenience to the citizens. The fallacies in this assertion were also brought out and identified. Before the Aadhaar scheme was rolled in, ration cards were issued based on certain exclusion and inclusion criteria.. The exclusion and inclusion criteria remain the same while another hurdle in the form of Aadhaar has been created. As India is still lacking in supporting infrastructure such as electricity, server connectivity among other things, Aadhaar is acting as a barrier rather than making it convenient for citizens to enroll in such schemes.The third benefit is fraud management. Here, a participant pointed out that this benefit was due to digitisation in the form of GPS chips in food delivery trucks and electronic payment and not the relational nature of Aadhaar. Aadhaar is only concerned with the linking up or relational part. About deduplication, it was pointed out how various government agencies have tackled it quite successfully by using technology different from biometrics which is unreliable at the best of times.

The Myths surrounding Aadhaar

The discussion also reflected on the fact that Aadhaar is often considered to be a panacea that subsumes all kinds of technologies to tackle leakages. However, this does not take into account the fact that leakages happen in many ways. A system should have been built to tackle those specific kinds of leakages, but the focus is solely on Aadhaar as the cure for all. Notably, participants who have been a part of the government pointed out how this myth is misleading and should instead be seen as the first step towards a more digitally enhanced country which is combining different technologies through one medium.

IndiaStack and FinTech Apps

What is India Stack?

The focus then shifted to another extremely important Big Data project, India Stack, being conceptualised and developed by a team of private developers called iStack, for the NPCI. It builds on the UID project, Jan Dhan Yojana and mobile services trinity to propagate and develop a cashless, presence-less, paperless and granular consent layer based on UID infrastructure to digitise India.

A participant pointed out that the idea of India Stack is to use UID as a platform and keep stacking things on it, such that more and more applications are developed. This in turn will help us to move from being a ‘data poor’ country to a ‘data rich’ one. The economic benefits of this data though as evidenced from the TAGUP report - a report about the creation of National Information Utilities to manage the data that is present with the government - is for the corporations and not the common man. The TAGUP report openly talks about privatisation of data.

Problems with India Stack

The granular consent layer of India Stack hasn’t been developed yet but they have proposed to base it on MIT Media Lab’s OpenPDS system. The idea being that, on the basis of the choices made by the concerned person, access to a person’s personal information may be granted to an agency like a bank. What is more revolutionary is that India Stack might even revoke this access if the concerned person expresses a wish to do so or the surrounding circumstances signal to India Stack that it will be prudent to do so. It should be pointed out that the the technology required for OpenPDS is extremely complex and is not available in India. Moreover, it’s not clear how this system would work. Apart from this, even the paperless layer has its faults and has been criticised by many since its inception, because an actual government signed and stamped paper has been the basis of a claim.. In the paperless system, you are provided a Digilocker in which all your papers are stored electronically, on the basis of your UID number. However, it was brought to light that this doesn’t take into account those who either do not want a Digilocker or UID number or cases where they do not have access to their digital records. How in such cases will people make claims?

A Digital Post-Dated Cheque: It’s Ramifications

A key change that FinTech apps and the surrounding ecosystem want to make is to create a digital post-dated cheque so as to allow individuals to get loans from their mobiles especially in remote areas. This will potentially cut out the need to construct new banks, thus reducing the capital expenditure , while at the same time allowing the credit services to grow. The direct transfer of money between UID numbers without the involvement of banks is a step to further help this ecosystem grow. Once an individual consents to such a system, however, automatic transfer of money from one’s bank accounts will be affected, regardless of the reason for payment. This is different from auto debt deductions done by banks presently, as in the present system banks have other forms of collateral as well. The automatic deduction now is only affected if these other forms are defaulted upon. There is no knowledge as to whether this consent will be reversible or irreversible. As Jan Dhan Yojana accounts are zero balance accounts, the account holder will be bled dry. The implication of schemes such as “Loan in under 8 minutes” were also discussed. The advantage of such schemes is that transaction costs are reduced.The financial institution can thus grant loans for the minimum amount without any additional enquiries. It was pointed out that this new system is based on living on future income much like the US housing bubble crash. Interestingly, in Public Distribution Systems, biometrics are insisted upon even though it disrupts the system. This can be seen as a part of the larger infrastructure to ensure that digital post-dated cheques become a success.

The Role of FinTech Apps

FinTech ‘apps’ are being presented with the aim of propagating financial inclusion. The Technology Advisory Group for Unique Projects report stated that as managing such information sources is a big task, just like electricity utilities, a National Information Utilities (NIU) should be set up for data sources. These NIUs as per the report will follow a fee based model where they will be charging for their services for government schemes. The report identified two key NIUs namely the National Payments Corporation of India (NPCI) and the Goods and Services Tax Network (GSTN). The key usage that FinTech applications will serve is credit scoring. The traditional credit scoring data sources only comprised a thin file of records for an individual, but the data that FinTech apps collect - a person’s UID number, mobile number. and bank account number all linked up, allow for a far more comprehensive credit rating. Government departments are willing to share this data with FinTech apps as they are getting analysis in return. Thus, by using UID and the varied data sources that have been linked together by UID, a ‘thick file’ is now being created by FinTech apps. Banking apps have not yet gone down the route of FinTech apps to utilise Big Data for credit scoring purposes.

The two main problems with such apps is that there is no uniform way of credit scoring. This distorts the rate at which a person has to pay interest. The consent layer adds another layer of complication as refusal to share mobile data with a FinTech app may lead to the app declaring one to be a risky investment thus, subjecting that individual to a higher rate of interest .

Regulation of FinTech Apps and the UID Infrastructure

India Stack and the applications that are being built on it, generate a lot of transaction metadata that is very intimate in nature. The privacy aspects of the UID legislation doesn't cover such data. The granular consent layer which has been touted to cover this still has to come into existence. Also, Big Data is based on sharing and linking of data. Here, privacy concerns and Big Data objectives clash. Big Data by its very nature challenges privacy principles like data minimisation and purpose limitation.The need for regulation to cover the various new apps and infrastructure which are being developed was pointed out.

Problems with UID

It has been observed that any problem present with Aadhaar is usually labelled as a teething problem, it’s claimed that it will be solved in the next 10 years. But, this begs the question - why is the system online right now?

Aadhaar is essentially a new data condition and a new exclusion or inclusion criteria. Data exclusion modalities as observed in Rajasthan after the introduction of biometric Point of Service (POS) machines at ration shops was found to be 45% of the population availing PDS services. This number also includes those who were excluded from the database by being included in the wrong dataset. There is no information present to tell us how many actual duplicates and how many genuine ration card holders were weeded out/excluded by POS.

It was also mentioned that any attempt to question Aadhaar is considered to be an attempt to go back to the manual system and this binary thinking needs to change. Big Data has the potential to benefit people, as has been evidenced by the scholarship and pension portals. However, Big Data’s problems arise in systems like PDS, where there is centralised exclusion at the level of the cloud. Moreover, the quantity problem present in the PDS and MNREGA systems persists. There is still the possibility of getting lesser grains and salary even with analysis of biometrics, hence proving that there are better technologies to tackle these problems. Presently, the accountability mechanisms are being weakened as the poor don’t know where to go to for redressal. Moreover, the mechanisms to check whether the people excluded are duplicates or not is not there. At the time of UID enrollment, out of 90 crores, 9 crore were rejected. There was no feedback or follow-up mechanism to figure out why are people being rejected. It was just assumed that they might have been duplicates.

Another problem is the rolling out of software without checking for inefficiencies or problems at a beta testing phase. The control of developers over this software, is so massive that it can be changed so easily without any accountability.. The decision making components of the software are all proprietary like in the the de-duplication algorithm being used by the UIDAI. Thus, this leads to a loss of accountability because the system itself is in flux, none of it is present in public domain and there are no means to analyse it in a transparent fashion..

These schemes are also being pushed through due to database politics. On a field study of NPR of citizens, another Big Data scheme, it was found that you are assumed to be an alien if you did not have the documents to prove that you are a citizen. Hence, unless you fulfill certain conditions of a database, you are excluded and are not eligible for the benefits that being on the database afford you.

Why is the private sector pushing for UIDAI and the surrounding ecosystem?

Financial institutions stand to gain from encouraging the UID as it encourages the credit culture and reduces transaction costs.. Another advantage for the private sector is perhaps the more obvious one, that is allows for efficient marketing of products and services..

The above mentioned fears and challenges were actually observed on the ground and the same was shown through the medium of a case study in West Bengal on the smart meters being installed there by the state electricity utility. While the data coming in from these smart meters is being used to ensure that a more efficient system is developed,it is also being used as a surrogate for income mapping on the basis of electricity bills being paid. This helps companies profile neighbourhoods. The technical officer who first receives that data has complete control over it and he can easily misuse the data. This case study again shows that instruments like Aadhaar and India Stack are limited in their application and aren’t the panacea that they are portrayed to be.

A participant pointed out that in the light of the above discussions, the aim appears to be to get all kinds of data, through any source, and once you have gotten the UID, you link all of this data to the UID number, and then use it in all the corporate schemes that are being started. Most of the problems associated with Big Data are being described as teething problems. The India Stack and FinTech scheme is coming in when we already know about the problems being faced by UID. The same problems will be faced by India Stack as well.

Can you opt out of the Aadhaar system and the surrounding ecosystem?

The discussion then turned towards whether there can be voluntary opting out from Aadhaar. It was pointed out that the government has stated that you cannot opt out of Aadhaar. Further, the privacy principles in the UIDAI bill are ambiguously worded where individuals only have recourse for basic things like correction of your personal information. The enforcement mechanism present in the UIDAI Act is also severely deficient. There is no notification procedure if a data breach occurs. . The appellate body ‘Cyber Appellate Tribunal’ has not been set up in three years.

CCTNS: Big Data and its Predictive Uses

What is Predictive Policing?

The next big Big Data case study was on the Crime and Criminal Tracking Network & Systems (CCTNS). Originally it was supposed to be a digitisation and interconnection scheme where police records would be digitised and police stations across the length and breadth of the country would be interconnected. But, in the last few years some police departments of states like Chandigarh, Delhi and Jharkhand have mooted the idea of moving on to predictive policing techniques. It envisages the use of existing statistical and actuarial techniques along with many other tropes of data to do so. It works in four ways: 1. By predicting the place and time where crimes might occur; 2. To predict potential future offenders; 3. To create profiles of past crimes in order to predict future crimes; 4. Predicting groups of individuals who are likely to be victims of future crimes.

How is Predictive Policing done?

To achieve this, the following process is followed: 1. Data collection from various sources which includes structured data like FIRs and unstructured data like call detail records, neighbourhood data, crime seasonal patterns etc. 2. Analysis by using theories like the near repeat theory, regression models on the basis of risk factors etc. 3. Intervention

Flaws in Predictive Policing and questions of bias

An obvious weak point in the system is that if the initial data going into the system is wrong or biased, the analysis will also be wrong. Efforts are being made to detect such biases. An important way to do so will be by building data collection practices into the system that protect its accuracy. The historical data being entered into the system is carrying on the prejudices inherited from the British Raj and biases based on religion, caste, socio-economic background etc.

One participant brought about the issue of data digitization in police stations, and the impact of this haphazard, unreliable data on a Big Data system. This coupled with paucity of data is bound to lead to arbitrary results. An effective example was that of black neighbourhoods in the USA. These are considered problematic and thus they are policed more, leading to a higher crime rate as they are arrested for doing things that white people in an affluent neighbourhood get away with. This in turn further perpetuates the crime rate and it becomes a self-fulfilling prophecy. In India, such a phenomenon might easily develop in the case of migrants, de-notified tribes, Muslims etc. A counter-view on bias and discrimination was offered here. One participant pointed out that problems with haphazard or poor quality of data is not a colossal issue as private companies are willing to fill this void and are actually doing so in exchange for access to this raw data. It was also pointed out how bias by itself is being used as an all encompassing term. There are multiplicities of biases and while analysing the data, care should be taken to keep it in mind that one person’s bias and analysis might and usually does differ from another. Even after a computer has analysed the data, the data still falls into human hands for implementation.

The issue of such databases being used to target particular communities on the basis of religion, race, caste, ethnicity among other parameters was raised. Questions about control and analysis of data were also discussed, i.e. whether it will be top-down with data analysis being done in state capitals or will this analysis be done at village and thana levels as well too. It was discussed as topointed out how this could play a major role in the success and possible persecutory treatment of citizens, as the policemen at both these levels will have different perceptions of what the data is saying. . It was further pointed out, that at the moment, there’s no clarity on the mode of implementation of Big Data policing systems. Police in the USA have been seen to rely on Big Data so much that they have been seen to become ‘data myopic’. For those who are on the bad side of Big Data, in the Indian context, laws like preventive detention can be heavily misused.There’s a very high chance that predictive policing due to the inherent biases in the system and the prejudices and inefficiency of the legal system will further suppress the already targeted sections of the society. A counterpoint was raised and it was suggested that contrary to our fears, CCTNS might lead to changes in our understanding and help us to overcome longstanding biases.

Open Knowledge Architecture as a solution to Big Data biases?

The conference then mulled over the use of ‘Open Knowledge’ architecture to see whether it can provide the solution to rid Big Data of its biases and inaccuracies if enough eyes are there. It was pointed out that Open Knowledge itself can’t provide foolproof protection against these biases as the people who make up the eyes themselves are predominantly male belonging to the affluent sections of the society and they themselves suffer from these biases.

Who exactly is Big Data supposed to serve?

The discussion also looked at questions such as who is this data for? Janata Information System (JIS), is a concept developed by MKSS where the data collected and generated by the government is taken to be for the common citizens. For e.g. MNREGA data should be used to serve the purposes of the labourers. The raw data as is available at the moment, usually cannot be used by the common man as it is so vast and full of information that is not useful for them at all. It was pointed out that while using Big Data for policy planning purposes, the actual string of information that turned out to be needed was very little but the task of unravelling this data for civil society purposes is humongous. By presenting the data in the right manner, the individual can be empowered. The importance of data presentation was also flagged. It was agreed upon that the content of the data should be for the labourer and not a MNC, as the MNC has the capability to utilise the raw data on it’s own regardless.

Concerns about Big Data usage

Participants pointed out that privacy concerns are usually brushed under the table due to a belief that the law is sufficient or that the privacy battle has already been lost.
In the absence of knowledge of domain and context, Big Data analysis is quite limited. Big Data’s accuracy and potential to solve problems needs to be factually backed.
The narrative of Big Data often rests on the assumption that descriptive statistics take over inferential statistics, thus eliminating the need for domain specific knowledge. It is claimed that the data is so big that it will describe everything that we need to know.
Big Data is creating a shift from a deductive model of scientific rigour to an inductive one. In response to this, a participant offered the idea that troves of good data allow us to make informed questions on the basis of which the deductive model will be formed. A hybrid approach combining both deductive and inductive might serve us best.
The need to collect the right data in the correct format, in the right place was also expressed.

Potential Research Questions & Participants’ Areas of Research

Following this discussion, participants brainstormed to come up with potential areas of research and research questions. They have been captured below:

Big Data, Aadhaar and India Stack:

Has Aadhaar been able to tackle illegal ways of claiming services or are local negotiations and other methods still prevalent?
Is the consent layer of India Stack being developed in a way that provides an opportunity to the UID user to give informed consent? The OpenPDS and its counterpart in the EU i.e. the My Data Structure were designed for countries with strong privacy laws. Importantly, they were meant for information shared on social media and not for an individual’s health or credit history. India is using it in a completely different sphere without strong data protection laws. What were the granular consent layer structures present in the West designed for and what were they supposed to protect?
The question of ownership of data needs to be studied especially in context of a globalised world where MNCs are collecting copious amounts of data of Indian citizens. What is the interaction of private parties in this regard?

Big Data and Predictive Policing:

How are inequalities being created through the Big Data systems? Lessons should be taken from the Western experience with the advent of predictive policing and other big data techniques - they tend to lead to perpetuation of the current biases which are already ingrained in the system.
It was also pointed out how while studying these topics and anything related to technology generally, we become aware of a divide that is present between the computational sciences and social sciences. This divide needs to be erased if Big Data or any kind of data is to be used efficiently. There should be a cross-pollination between different groups of academics. An example of this can be seen to be the ‘computational social sciences departments’ that have been coming up in the last 3-4 years.
Why are so many interim promises made by Big Data failing? A study of this phenomenon needs to be done from a social science perspective. This will allow one to look at it from a different angle.

Studying Big Data:

What is the historical context of the terms of reference being used for Big Data? The current Big Data debate in India is based on parameters set by the West. For better understanding of Big Data, it was suggested that P.C. Mahalanobis’ experience while conducting the Indian census, (which was the Big Data of that time) can be looked at to get a historical perspective on Big Data. This comparison might allow us to discover questions that are important in the Indian context. It was also suggested that rather than using ‘Big Data’ as a catchphrase to describe these new technological innovations, we need to be more discerning.
What are the ideological aspects that must be considered while studying Big Data? What does the dialectical promise of technology mean? It was contended that every time there is a shift in technology, the zeitgeist of that period is extremely excited and there are claims that it will solve everything. There’s a need to study this dialectical promise and the social promise surrounding it.
Apart from the legitimate fears that Big Data might lead to exclusion, what are the possibilities in which it improve inclusion too?
The diminishing barrier between the public and private self, which is a tangent to the larger public-private debate was mentioned.
How does one distinguish between technology failure and process failure while studying Big Data?

Big Data: A Friend?

In the concluding session, the fact that the Big Data moment cannot be wished away was acknowledged. The use of analytics and predictive modelling by the private sector is now commonplace and India has made a move towards a database state through UID and Digital India. The need for a nuanced debate, that does away with the false equivalence of being either a Big Data enthusiast or a luddite is crucial.

A participant offered two approaches to solving a Big Data problem. The first was the Big Data due process framework which states that if a decision has been taken that impacts the rights of a citizen, it needs to be cross examined. The efficacy and practicality of such an approach is still not clear. The second, slightly paternalistic in nature, was the approach where Big Data problems would be solved at the data science level itself. This is much like the affirmative algorithmic approach which says that if in a particular dataset, the data for the minority community is not available then it should be artificially introduced in the dataset. It was also suggested that carefully calibrated free market competition can be used to regulate Big Data. For e.g. a private personal wallet company that charges higher, but does not share your data at all can be an example of such competition.

Another important observation was the need to understand Big Data in a Global South context and account for unique challenges that arise. While the convenience of Big Data is promising, its actual manifestation depends on externalities like connectivity, accurate and adequate data etc that must be studied in the Global South.

While the promises of Big Data are encouraging, it is also important to examine its impacts and its interaction with people's rights. Regulatory solutions to mitigate the harms of big data while also reaping its benefits need to evolve.

For more details visit https://cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report