A Case Study by
The Centre for Internet & Society
Bangalore, India
Elonnai Hickok . Amber Sinha . Vanya Rakesh . Scott Mason . Vipul Kharbanda . Sunil Abraham
Images by Pooja Saxena
Big Data is still in nascent phases in India. Ways in which Big Data is being envisioned and used in Governance include:
Informing policy and decisions: Ex - MyGov.in is a crowd sourcing platform for citizen opinion and input that leverages Big Data technologies to inform policy decisions.
Improving delivery of government services: Ex - Aadhaar and Digital India seek to improve the delivery of government services through means such as reducing fraud, making delivery more efficient, etc.
Assumptions that have been made about big data from policy documents, news items, and conference presentations include:
Digital India is Government of India’s flagship programme to enhance inter-operability of government services and departments and to transform India into a digital knowledge economy.
The initiative focuses on e-Governance policy initiatives by using ICT to offer solutions, digitally empower citizens and provide government benefits and services transparently.
The Nine pillars of Digital India involve the use of technology in areas like Broadband connectivity, electronic delivery of services by way of e-Kranti initiative, encouraging Open data platform, etc. Other programmes include Digital Locker, eSign, and mygov.in
Digital India is a part of a trend in governance which focuses on creating platforms and large datasets based on the collection and analysis of large amounts of data.
Modernizing of Government data centres is being done by commercial entities - particularly foreign entities
The lack of interoperability between data sets will stand as a barrier to the success of Digital India and should be a national priority
The UID project was conceived by the Planning Commission under the UIDAI (established in the year 2009).
The objective of the scheme has been to issue a 12-digit unique identification number by the Unique Identification Authority of India (Aadhar card/number) to residents, which can be authenticated and verified online.
The Aadhaar number forms a crucial part of the vision for the Digital India programme.
A draft legislation has been proposed, but not passed. The UIDAI has organizational policies, MOUs, and recommendary policies in place.
UIDAI may face operational challenges and issues with the accuracy of data
In light of the shift towards urban transformation due to massive influx of migrants from villages in India, the Indian Government envisioned building 100 smart cities across the country over the span of five years..
In August this year, 98 smart cities across India have been unveiled for this Project.
The Mission will be operated as a Centrally Sponsored Scheme (CSS) and the Central Government proposes to give financial support to the Mission to the extent of Rs. 48,000 crores over five years (on an average Rs. 100 crore per city per year).
Smart Cities Mission aims to drive economic growth in the country, make the cities livable, inclusive and improve the quality of life.
No specific enabling legislation or policy has been proposed but a draft concept note and a mission document have been published.
In the Smart City scheme, technology is being relied upon to 'smooth over' city level problems.
Smart Cities bring together open data and Big Data.
Smart government by enabling e-governance, improved models for future development, better decision-making, efficient service delivery, and making the government more transparent, participatory and accountable.
Smart people by creating a more informed citizenry and fostering creativity, inclusivity, empowerment and participation.
The timeline for the implementation the Smart City initiative is too short for what it seeks to achieve
The Smart City initiative assumes that the technology is neutral and the reality of urban data politics are not being considered
The Smart City initiative raises questions of socio-spatial consequences
The Smart City initiative has not considered the need for interoperable standards
There is a lack of inter-departmental and organizational cooperation, which is needed
Smart Cities risk exclusion and marginalization
Smart Cities are an example of a western practice being imposed in the Indian context
Smart Cities represent a top-down application of technology
Areas in which India's current data protection standards could be insufficient in a 'Big Data' scenario include:
There are potential legal hurdles in the collection and use of different types of digital data. For example
Decision-Making - Big Data is providing governments and businesses with unprecedented opportunities to create new insights and solutions; becoming more responsive to new opportunities and better able to act quickly - and in some cases preemptively – to deal with emerging threats.
Efficiency and Productivity - By providing the information and analysis needed for organisations to better manage and coordinate their operations; Big Data can help to reduce waste, leading to the better utilization of scarce resources and a more productive workforce. (Kshetri, 2014)
R & D and Innovation - Big Data can help businesses to gain an understanding of how others perceive their products or identify customer demand and adapt their marketing or indeed the design of their products accordingly. (Tucker and Welford, 2014)
Personalisation - By enabling companies to generate in-depth profiles of their customers, Big Data allows businesses to quickly and cost-effectively adapt their services to better meet customer demands. (Tucker and Welford, 2014)
Transparency - Advances in data analytics can give consumers and citizens the knowledge to hold governments and businesses to account, as well as make more informed choices about the products and services they use. ( Brown, Chui, and Manyika, 2011)
Re-identification – Big Data potentially allows for the re-identification of anonymized user data by cross-referencing multiple datasets. (Tene and Polonetsky, 2013)
Collection Limitation and Data Minimization – The proliferation of internet enabled devices as well as Big Data’s inherent need to collect as much data as possible, is making these principles of privacy obsolete (Barocas and Selbst, 2015)
Purpose Limitation – Big Data increasingly requires data to be processed several times for a variety of different purposes undermining this principle of privacy. (Article 29 Data Protection Working Party, 2014)
Access and Correction – The real-time generation and analysis of Big Data is challenging the principles of user access and correction. (Article 29 Data Protection Working Party, 2014)
Notice – As a result of Big Data practices relying on vast amounts of data from numerous sources and the re-use of that data - the principle of notice is changing. (Tene and Polonetsky, 2013)
Opt In-Out – The proliferation of internet-enabled devices, their integration into the built environment and the real-time nature of data collection and analysis means that the opting out of data collection is becoming more difficult. (Oxford Internet Institute, 2015)
“Chilling-Effects” – the normalization of large scale data collection risks producing a widespread perception of ubiquitous surveillance, thereby generating so-called ‘chilling effects’ on user’s behavior and free speech. (Matthews and Tucker, 2015)
Dignitary Harms – the automated nature of Big Data analytics possess the potential to reveal personal or sensitive insights about users.
For many the principle of consent has become unworkable in an age of pervasive data collection. Specifically within the literature the following problems with the consent have been identified.
Cognition
Failure to read/access terms of use policies (inaccessible, click-through etc.)
Failure to understand terms of use policies (illiteracy, complexity of legal terminology etc.)
Failure to fully anticipate or comprehend the potential long-term consequences of providing consent.
Opt-in/out
Binary nature of consent
Effectiveness of opt-out?
Structural Problems
Scale (data minimization, collection limitation)
Aggregation
Purpose Limitation
Counter-productive?
Anti-Competitive – The inevitable inequalities in access to user data between start-ups and large well established companies risks leading to a reduction in competition. (Newman, 2014)
Research – Big Data can create inequalities in access to data for researchers and journalist leading to the inability to replicate experiments or verify findings. (Boyd and Crawford, 2012)
Global Inequality – lower levels of connectivity, poor information infrastructure, under-investment in information technologies and a lack of skills make it far more difficult for the developing world to fully reap the rewards of Big Data, thereby potentially deepening global economic inequality.
Data Dispersion – The duplication and dispersion of data across many different data repositories in order to optimize query processing, makes it more difficult for organizations to locate and secure all items of confidential information.
Honey Pot – The larger the quantities of confidential information stored by companies on their databases the more attractive those databases appear to potential hackers
Injudicious or discriminatory outcomes - faults in the programming of Big Data algorithms or discriminatory assessment criteria can have potentially discriminatory effects, reinforcing existing social inequalities. (Robinson and Yu, 2014)
Lack of Transparency - Given their importance algorithms are closely guarded by companies and often classified as trade secrets, meaning there is very little transparency or accountability regarding chronic lack of accountability and transparency in terms of how Big Data algorithms are programmed or what criteria are used to determine outcomes. (Barocas and Selbst, 2015)
Obfuscation – The sheer quantity of correlations and insights identifiable within data sets can sometimes risk obscuring key insights. (Boyd and Crawford, 2012)
“Apophenia” – a phenomena whereby analysts interpret patterns where none exist, ‘simply because enormous quantities of data can offer connections that radiate in all directions’ (Boyd and Crawford, 2012)
From Causality to Correlation – Big Data’s emphasis on correlative analysis risks leading to an abandonment of the pursuit of causal knowledge in favour of shallow descriptive accounts of scientific phenomena (Boyd and Crawford, 2012)
“End of Theory”?/The Data does NOT speak for itself – suggestions that ‘the data speaks for itself’ neglects domain specific knowledge leading to interpretations which fail to embedded the results within wider scientific debates or knowledge. (Kitchen, 2015)
‘N=all’ – Whilst Big Data may seem to be exhaustive in its scope, it can be considered to be so only in relation to the particular ontological and methodological framework chosen by the researcher. No data set however large can fully account for all information relevant to a given phenomenon, in particular unquantifiable and undatafiable variables.
‘Correlation is enough’ – For many the use of Big Data analytics signals a worrying transition from deductive to inductive reasoning. Although Big Data can demonstrate interesting correlations these patterns alone are not enough to provide an explanatory account of the phenomena.
The Data speaks for itself’ – Despite claims that Big Data can be interpreted by anyone and that all correlations are inherently meaningful, without domain experts to contextualise the results the predictive and explanatory utility of big data is nonetheless limited, and can sometimes lead to spurious conclusions.
Beyond privacy policies, transparency of data flow is critical.
For some schemes there is lack of legal framework for collection and use of data.
Broadly data is being equated as truth and seen as the solution
Public private partnerships complicate issues of liability and data ownership.
Public Private partnerships create a 'black box' around data practices of both the government and private companies.
Because India's data protection standards do not apply to the public sector, transparency of the public private relationship is critical.
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