Centre for Internet and Society

New Media, personalisation and the role of algorithms

amber — 2017-01-16T07:20:52Z

In his much acclaimed book, The Filter Bubble, Eli Pariser explains how personalisation of services on the web works and laments that they are creating individual bubbles for each user, which run counter to the idea of the Internet as an inherently open place. While Pariser’s book looks at the practices of various large companies providing online services, he briefly touches upon the role of new media such as search engines and social media portals in new curation. Building upon Pariser’s unexplored argument, this article looks at the impact of algorithmic decision-making and Big Data in the context of news reporting and curation.

Everything which bars freedom and fullness of communication sets up barriers that divide human beings into sets and cliques, into antagonistic sects and factions, and thereby undermines the democratic way of life. —John Dewey

Eli Pariser, in his book, The Filter Bubble,[1] refers to the scholarship by Walter Lippmann and John Dewey as integral to the evolution of the understanding of the democratic and ethical duties of the Fourth Estate. Lippmann was disillusioned by the role of newspapers in propaganda for the First World War. He responded with three books in quick succession — Liberty and the News,[2] Public Opinion[3] and The Phantom Public.[4] Lippmann brought attention the fact that the process of news-reporting was conducted through privately determined and unexamined standards. The failure of the Fourth Estate to perform its democratic functions, was, in the opinion of Lippmann, one of the prime factors responsible for the public not being an informed and rational entity. John Dewey, while rejecting Lippmann’s arguments that matters of public policy can only be determined by inside experts with training and education, did acknowledge the his critique of the media.

Pariser points to the creation of a wall between editorial decisionmaking and advertiser interests, as the eventual result of the Lippmann and Dewey debate. While accepting that this division between the financial and reporting sides of media houses has not been always observed, Pariser emphasises that the fact that the standard exists is important.[5] Unlike traditional media, the new media which relies on algorithmic decision-making for personalisation is not subject to the same standards which try to mitigate the influence of commercial interests on editorial decisions while performing many of the same functions as the traditional media.[6]

How personalisation algorithms work

Kevin Slavin, at his famous talk in the TEDGLobal Conference, characterised algorithms as “maths that computers use to decide stuff” and that it was infiltrating every aspect of our lives.[7] According to Slavin’s view, algorithms can be seen as control technologies and shape our world constantly through media and information systems, dynamically modifying content and function through these programmed routines. Search engines and social media platforms perpetually rank user-generated content through algorithms.[8]

Personalisation technologies have various advantages. It translates into more relevant content, which for service providers means more clicks and revenue and for consumer, less time spent on finding the content.[9] However, it also leads to privacy compromise, lack of control and reduced individual capability.[10] Search engines like Google use the famous PageRank algorithm, which combined with geographical location and previous searches yields most relevant search results.[11] PageRank algorithm uses various real time variables dependent on both voluntary and involuntary user inputs. These variables include number of clicks, number of occurrences of the key terms and number of references by other credible pages etc. This data in turn determines the order of pages in search results and influences the way we perceive, understand and analyse information.[12] Maps showing real time traffic information retrieve data from laser and infrared sensors alongside the road and from information from devices of users. Once this real time data is combined with historical trends, these maps recommend rout to every user, hence influencing the traffic patterns.[13]

Even though this phenomenon of personalization may appears to be new, it has been prevalent in the society for ages.[14] The history of mass media culture clearly shows personalization has always been a method to increase market, market reach and customer satisfaction.[15] Newspapers have sections dedicated to special topics, radio and TV have channels dedicated to different interest groups, age groups and consumers.[16] These personalised sections in a newspaper and personalised channels on radio and television don’t just provide greater satisfaction to the readers or listeners or consumers, they also provide targeted advertisement space for the advertisers and content developers. However, digital footprints and mass collection of data have made this phenomenon much more granular and detailed. Geographical location of an individual can tell a lot about their community, their culture and other important traits local to a community.[17] This data further assists in personalisation. Current developments in technology not only help in better collection of data about personal preferences but also help in better personalisation.

Pariser mentions three ways in which the personalization technologies of this day are different from those of the past. First, for the very first time, individuals are alone in the filter bubble. While in traditional forms of personalisation, there were various individuals who shared the same frame of reference, now there is a separate sets of filters governing the dissemination of content to each individual.[18] Second, the personalisation technologies are entirely invisible now, and there is little that consumers can do to control or modify them.[19] Third, often the decision to be subject to these personalisation technologies is not an informed choice. A good example of this would be an individual’s geographical location.[20]

The neutrality of New Media?

More and more, we have noticed personalisation technologies having an impact on how we consume news on the Internet. Google News, Facebook’s News Feed which tries to put together a dynamic feed for both personal and global stories, and Twitter’s trending hashtag feature, have brought forward these services are key drivers of an emerging news ecosystem. Initially, this new media was hailed as a natural consequence of the Internet which would enable greater public participation, allow journalists to find more stories and engage with the readers directly. An illustration of the same could be seen in the way Internet based news media and social networking websites behaved in the aftermath of Israel’s attacks on a United Nations run school in Gaza strip. While much of the international Internet media covered the story, Israel’s home media did not cover the story. The only exception to this was the liberal Israeli news website Ha’aretz.[21] Network graph details of Twitter, for a few days immediately after the incident clearly show the social media manifestation of the event in the personalised cyberspace. It is clearly visible that when most of the word was re-tweeting news of this heinous act of Israel, Israeli’s hardly re-tweeted this news. In fact they were busty re-tweeting the news of rocket attacks on Israel.[22]

The use of social media in newsmaking was hailed by many scholars as symptomatic of the decentralisation characteristic of the Internet. It has been seen as movement towards greater grassroots participation by negating the ‘gatekeeping’ role traditionally played by editors. Thomas Poell and José van Dijck punch holes in theory of social media and other online technologies as mere facilitators of user participation and translators of user preferences through Big Data analytics.[23] They quote T. Gillespie’s work which talks of the narrative of these online services as platforms which are “open, neutral, egalitarian and progressive support for activity.”[24]

Pedro Domingos calls the overwhelming number of choices as the defining problem of the information age, and machine learning and data analytics as the largest part of this solution.[25] The primary function of algorithmic decision making in the context of consumption of content is to narrow down the choices. Domingos is more optimistic about the impact of these technologies, and he says “last step of the decision is usually still for humans to make, but learners intelligently reduce the choices to something a human can manage.”[26] On the other hand, Pariser is more circumspect about the coercive result of machine learning algorithms. Whichever way we lean, we have to accept that a large part of personalisation algorithms is to select and prioritize content by categorising it on the basis of relevance and popularity.

Poell and van Dijck call this a new knowledge logic which in effect replaces human judgement (as, earlier exercised by editors) to some kind of proxy decisionmaking based on data. Their main thesis is that there is little evidence to suggest that the latter is more democratic than former and creates new problems of its own. They go on to compare the practices of various services including Facebook’s new graph and Twitter’s trending topic, and conclude that they prioritise breaking news stories over other kinds of content.[27] For instance, the algorithm for the trending topics depends not on the volume but the velocity of the tweets with the hashtag or term. It could be argued that given this predilection, the algorithms will rarely prefer complex content. If we go by Lippmann and Dewey’s idea that the role of the Fourth Estate is to inform public debate and accountability of those in positions of power, this aspect of Big Data algorithms does not correspond with this role.

Quantified Audience

Another aspect of use of Big Data and algorithms in New Media that requires attention is that the networked infrastructure enables a quantified audience. C W Anderson who has studied newsroom practices in the US looked at role played by audience quantification and rationalization in shifting newswork practices. He concluded that more and more, journalists are less autonomous in their news decisions and increasingly reliant on audience metrics as a supplement to news judgment.[28] Poell and van Dijck review the the practices by some leading publications such a New York Times, L.A. Times and Huffington Post, and degree to which audience metrics dictates editorial decisions. While New York Times seems to prioritise content on their social media portals based on expectation of spike in user traffic, L.A. Times goes one step further by developing content specifically aimed towards promoting greater social participation. Neither of these practices though compare to the reliance on SEO and SMO strategies of web-born news providers like Huffington Post. They have traffic editors who trawl the Internet for trending topics and popular search terms, the feedback from them dictates the content creation.[29]

Conclusion

The above factors demonstrate that the idea of New Media leading to the Fourth Estate performing its democratic functions does not take into account the actual practices. This idea is based on the erroneous assumption that technology, in general and algorithms, in particular are neutral. While the emergence of New Media might have reduced the gatekeeping role played by the editors, its strong prioritisation of content that will be popular reduce the validity of arguments that it leads to more informed public discussion. As Pariser said, the traditional media scores over the New Media inasmuch as there is an existence of a standard of division between editorial decisionmaking and advertiser interest. While this standard is flouted by media houses all the time, it exists as a metric to aspire to and measure service providers against. The New Media performs many of the same functions and maybe it is time to evolve some principles and ethical standards that take into account the need for it to perform these democratic functions.

Endnotes

^{^[1]} Eli Pariser, The Filter Bubble: What the Internet is hiding from you (The Penguin Press, New York, 2011)

[2] Walter Lippmann, Liberty and News (Harcourt, Brace and Howe, New York 1920) available athttps://archive.org/details/libertyandnews01lippgoog

^{^[3]} Walter Lippmann, Public Opinion (Harcourt, Brace and Howe, New York 1920) available at http://xroads.virginia.edu/~Hyper2/CDFinal/Lippman/cover.html

^{^[4]} Walter Lippmann, The Phantom Public (Transaction Publishers, New York, 1925)

^{^[5]} Supra Note 1 at 35.

^{^[6]} Supra Note 1 at 36.

^{^[7]} https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world/transcript?language=en

^{^[8]} Fenwick McKelvey, “Algorithmic Media Need Democratic Methods: Why Publics Matter”, available at http://www.fenwickmckelvey.com/wp-content/uploads/2014/11/2746-9231-1-PB.pdf.

^{^[9]} http://mashable.com/2011/06/03/filters-eli-pariser/#9tIHrpa_9Eq1

^{^[10]} Helen Ashman, Tim Brailsford, Alexandra Cristea, Quan Z Sheng, Craig Stewart, Elaine Torns and Vincent Wade, “The ethical and social implications of personalization technologies for e-learning” available at http://www.sciencedirect.com/science/article/pii/S0378720614000524.

^{^[11]} Sergey Brin and Lawrence Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine” available at http://infolab.stanford.edu/pub/papers/google.pdf.

^{^[12]} Ian Rogers, “The Google Pagerank Algorithm and How It Works” available at http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm.

^{^[13]} Trygve Olson and Terry Nelson, “The Internet’s Impact on Political Parties and Campaigns”, available at http://www.kas.de/wf/doc/kas_19706-544-2-30.pdf?100526130942.

^{^[14]} Ian Witten, “Bias, privacy and and personalisation on the web”, available at http://www.cs.waikato.ac.nz/~ihw/papers/07-IHW-Bias,privacyonweb.pdf.

^{^[15]} Supra Note 1 at 10.

^{^[16]} https://www.americanpressinstitute.org/publications/reports/survey-research/social-demographic-differences-news-habits-attitudes/

^{^[17]} Charles Heatwole, “Culture: A Geographical Perspective” available at http://www.p12.nysed.gov/ciai/socst/grade3/geograph.html.

^{^[18]} Supra Note 1 at 10.

^{^[19]} Id.

^{^[20]} Supra Note 1 at 11.

^{^[21]} Paul Mason, “Why Israel is losing the social media war over Gaza?” available at http://blogs.channel4.com/paul-mason-blog/impact-social-media-israelgaza-conflict/1182.

^{^[22]} Gilad Lotan, Israel, Gaza, War & Data: Social Networks and the Art of Personalizing Propaganda available at www.huffingtonpost.com/entry/israel-gaza-war-social-networks-data_b_5658557.html

^{^[23]} Thomas Poell and José van Dijck, “Social Media and Journalistic Independence” in Media Independence: Working with Freedom or Working for Free?, edited by James Bennett & Niki Strange. (Routledge, London, 2015)

^{^[24]} T Gillespie, “The politics of ‘platforms,” in New Media & Society (Volume 12, Issue 3).

^{^[25]} Pedro Domingos, The Master Algorithm: How the quest for the ultimate learning machine will re-make the world (Basic Books, New York, 2015) at 38.

^{^[26]} Ibid at 40.

^{^[27]} Supra Note 23.

^{^[28]} C W Anderson, Between creative and quantified audiences: Web metrics and changing patterns of newswork in local US newsrooms, available at https://www.academia.edu/10937194/Between_Creative_And_Quantified_Audiences_Web_Metrics_and_Changing_Patterns_of_Newswork_in_Local_U.S._Newsrooms

^{^[29]} Supra Note 23.

For more details visit https://cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms

Adoption of Standards in Smart Cities - Way Forward for India

vanya — 2016-04-11T03:04:46Z

With a paradigm shift towards the concept of “Smart Cities’ globally, as well as India, such cities have been defined by several international standardization bodies and countries, however, there is no uniform definition adopted globally. The glue that allows infrastructures to link and operate efficiently is standards as they make technologies interoperable and efficient.

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Globally, the pace of urbanization is increasing exponentially. The world’s urban population is projected to rise from 3.6 billion to 6.3 billion between 2011 and 2050. A solution for the same has been development of sustainable cities by improving efficiency and integrating infrastructure and services [1]. It has been estimated that during the next 20 years, 30 Indians will leave rural India for urban areas every minute, necessitating smart and sustainable cities to accommodate them [2]. The Smart Cities Mission of the Ministry of Urban Development was announced in the year 2014, followed by selection of 100 cities in the year 2015 and 20 of them being selected for the first Phase of the project in the year 2016. The Mission [3] lists the “core infrastructural elements” that a smart city would incorporate like adequate water supply, assured electricity, sanitation, efficient public transport, affordable housing (especially for the poor), robust IT connectivity and digitisation, e-governance and citizen participation, sustainable environment, safety and security for citizens, health and education.

With a paradigm shift towards the concept of “Smart Cities’ globally, as well as India, such cities have been defined by several international standardization bodies and countries, however, there is no uniform definition adopted globally. The envisioned modern and smart city promises delivery of high quality services to the citizens and will harness data capture and communication management technologies. The performance of such cities would be monitored on the basis of physical as well as the social structure comprising of smart approaches and solution to utilities and transport.

The glue that allows infrastructures to link and operate efficiently is standards as they make technologies interoperable and efficient. Interoperability is essential and to ensure smart integration of various systems in a smart city, internationally agreed standards that include technical specifications and classifications must be adhered to. Development of international standards ensure seamless interaction between components from different suppliers and technologies [4].

Standardized indicators within standards benefit smart cities in the following ways:

Effective governance and efficient delivery of services.
International and Local targets, benchmarking and planning.
Informed decision making and policy formulation.
Leverage for funding and recognition in international entities.
Transparency and open data for investment attractiveness.
A reliable foundation for use of big data and the information explosion to assist cities in building core knowledge for city decision-making, and enable comparative insight.

The adoption of standards for smart cities has been advocated across the world as they are perceived to be an effective tool to foster development of the cities. The Director of the ITU Telecommunication Standardization Bureau Chaesub Lee is of the view that “Smart cities will employ an abundance of technologies in the family of the Internet of Things (IoT) and standards will assist the harmonized implementation of IoT data and applications , contributing to effective horizontal integration of a city’s subsystems” [5].

Smart Cities standards in India

National Association of Software and Services Companies (NASSCOM) partnered with Accenture [6] to prepare a report called ‘Integrated ICT and Geospatial Technologies Framework for 100 Smart Cities Mission’ [7] to explore the role of ICT in developing smart cities [8], after the announcement of the Mission by Indian Government. The report, released in May 2015, lists down 55 global standards, keeping in view several city sub-systems like urban planning, transport, governance, energy, climate and pollution management, etc which could be applicable to the smart cities in India.

Though NASSCOM is working closely with the Ministry of Urban Development to create a sustainable model for smart cities [9], due to lack of regulatory standards for smart cities, the Bureau of Indian Standards (BIS) in India has undertaken the task to formulate standardised guidelines for central and state authorities in planning, design and construction of smart cities by setting up a technical committee under the Civil engineering department of the Bureau. However, adoption of the standards by implementing agencies would be voluntary and intends to complement internationally available documents in this area [10].

Developing national standards in line with these international standards would enable interoperability (i.e. devices and systems working together) and provide a roadmap to address key issues like data protection, privacy and other inherent risks in the digital delivery and use of public services in the envisioned smart cities, which call for comprehensive data management standards in India to instill public confidence and trust [11].

Key International Smart Cities Standards

Following are the key internationally accepted and recognized Smart Cities standards developed by leading organisations and the national standardization bodies of several countries that India could adopt or develop national standards in line with these.

The International Organization for Standardization (ISO) - Smart Cities Standards

ISO is an instrumental body advocating and developing for smart cities to safeguard rights of the people against a liveable and sustainable environment. The ISO Smart Cities Strategic Advisory Group uses the following working definition: A ‘Smart City’ is one that dramatically increases the pace at which it improves its social, economic and environmental (sustainability) outcomes, responding to challenges such as climate change, rapid population growth, and political and economic instability by fundamentally improving how it engages society, how it applies collaborative leadership methods, how it works across disciplines and city systems, and how it uses data information and modern technologies in order to transform services and quality of life for those in and involved with the city (residents, businesses, visitors), now and for the foreseeable future, without unfair disadvantage of others or degradation of the natural environment. [For details see ISO/TMB Smart Cities Strategic Advisory Group Final Report, September 2015 ( ISO Definition, June 2015)].

The ISO Technical Committee 268 works on standardization in the field of Sustainable Development in Communities [12] to encourage the development and implementation of holistic, cross-sector and area-based approaches to sustainable development in communities. The Committee comprises of 3 Working Groups [13]:

Working Group 1: System Management ISO 37101- This standard sets requirements, guidance and supporting techniques for sustainable development in communities. It is designed to help all kinds of communities manage their sustainability, smartness and resilience to improve the contribution of communities to sustainable development and assess their performance in this area [14].
Working Group 2 : City Indicators- The key Smart Cities Standards developed by ISO TC 268 WG 2 (City Indicators) are:

ISO 37120 Sustainable Development of Communities — Indicators for City Services and Quality of Life

One of the key standards and an important step in this regard was ISO 37120:2014 under the ISO’s Technical Committee 268 (See Working on Standardization in the field of Sustainable Development in Communities) providing clearly defined city performance indicators (divided into core and supporting indicators) as a benchmark for city services and quality of life, along with a standard approach for measuring each for city leaders and citizens [15]. The standard is global in scope and can help cities prioritize city budgets, improve operational transparency, support open data and applications [16]. It follows the principles [17] set out and can be used in conjunction with ISO 37101.

ISO 37120 was the first ISO Standard on Global City Indicators published in the year 2014, developed on the basis of a set of indicators developed and extensively tested by the Global City Indicators Facility (a project by University of Toronto) and its 250+ member cities globally. GCIF is committed to build standardized city indicators for performance management including a database of comparable statistics that allow cities to track their effectiveness on everything from planning and economic growth to transportation, safety and education [18].

The World Council on City Data (WCCD) [19] - a sister organization of the GCI/GCIF - was established in the year 2014 to operationalize ISO 37120 across cities globally. The standards encompasses 100 indicators developed around 17 themes to support city services and quality of life, and is accessible through the WCCD Open City Data Portal which allows for cutting-edge visualizations and comparisons. Indian cities are not yet listed with WCCD [20].

The indicators are listed under the following heads [21]:

Economy
Education
Environment
Energy
Finance
Fire and Emergency Responses
Governance
Health
Safety
Shelter
Recreation
Solid Waste
Telecommunication and innovation
Transportation
Urban Planning
Waste water
Water and Sanitation

This International Standard is applicable to any city, municipality or local government that undertakes to measure its performance in a comparable and verifiable manner, irrespective of size and location or level of development. City indicators have the potential to be used as critical tools for city managers, politicians, researchers, business leaders, planners, designers and other professionals [22]. The WCCD forum highlights need for cities to have a set of globally standardized indicators to [23]:

Manage and make informed decisions through data analysis
Benchmark and target
Leverage Funding with senior levels of government
Plan and establish new frameworks for sustainable urban development
Evaluate the impact of infrastructure projects on the overall performance of a city.

ISO/DTR 37121- Inventory and Review of Existing Indicators on Sustainable Development and Resilience in Cities

The second standard under ISO TC 268 WG 2 is ISO 37121, which defines additional indicators related to sustainable development and resilience in cities. Some of the indicators include: Smart Cities, Smart Grid, Economic Resilience, Green Buildings, Political Resilience, Protection of biodiversity, etc. The complete list can be viewed on the Resilient Cities website [24].

Working Group 3: Terminology - There are no publicly available documents so far, giving details about the status of the activities of this group. The ISO Technical Committee 268 also includes Sub Committee 1 (Smart Community Infrastructure) [25], comprising of the following Working Groups: 1) WG 1 Infrastructure metrics, and 2) WG 2 Smart Community Infrastructure.

The key Smart Cities Standards developed by ISO under this are:

ISO 37151:2015 Smart community infrastructures — Principles and Requirements for Performance Metrics
In the year 2015, a new ISO technical specification for smart cities- 37151:2015 for Principles and requirements for performance metrics was released. The purpose of standardization in the field of smart community infrastructures such as energy, water, transportation, waste, information and communications technology (ICT), etc. is to promote the international trade of community infrastructure products and services and improve sustainability in communities by establishing harmonized product standards [26]. The metrics in this standard will support city and community managers in planning and measuring performance, and also compare and select procurement proposals for products and services geared at improving community infrastructures [27].
This Technical Specification gives principles and specifies requirements for the definition,identification, optimization, and harmonization of community infrastructure performance metrics, and gives recommendations for analysis, regarding interoperability, safety, security of community infrastructures [28]. This new Technical Specification supports the use of the ISO 37120 [29].
ISO/TR 37150:2014 Smart Community Infrastructures - Review of Existing Activities Relevant to Metrics
This standard addresses community infrastructures such as energy, water, transportation, waste and information and communications technology (ICT). Smart community infrastructures take into consideration environmental impact, economic efficiency and quality of life by using information and communications technology (ICT) and renewable energies to achieve integrated management and optimized control of infrastructures. Integrating smart community infrastructures for a community helps improve the lifestyles of its citizens by, for example: reducing costs, increasing mobility and accessibility, and reducing environmental pollutants.
ISO/TR 37150 reviews relevant metrics for smart community infrastructures and provides stakeholders with a better understanding of the smart community infrastructures available around the world to help promote international trade of community infrastructure products and give information about leading-edge technologies to improve sustainability in communities [30]. This standard, along with the above mentioned standards [31] supports the multi-billion dollar smart cities technology industry.

Several other ISO Working Groups developing standards applicable to smart and sustainable cities have been listed in our website [32].

The International Telecommunications Union (ITU)

The ITU is another global body working on development of standards regarding smart cities.

A study group was formed in the year 2015 to tackle standardization requirements for the Internet of Things, with an initial focus on IoT applications in smart cities to address urban development challenges [33], to enable the coordinated development of IoT technologies, including machine-to-machine communications and ubiquitous sensor networks. The group is titled “ITU-T Study Group 20: IoT and its applications, including smart cities and communities”, established to develop standards that leverage IoT technologies to address urban-development challenges and the mechanisms for the interoperability of IoT applications and datasets employed by various vertically oriented industry sectors [34].

ITU-T also concluded a focused study group looking at smart sustainable cities in May 2015, acting as an open platform for smart city stakeholders to exchange knowledge in the interests of identifying the standardized frameworks needed to support the integration of ICT services in smart cities. Its parent group is ITU-T Study Group 5, which has agreed on the following definition of a Smart Sustainable City:
"A smart sustainable city is an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects".

UK - British Standards Institution

Apart from the global standards setting organisations, many countries have been looking at developing standards to address the growth of smart cities across the globe. In the UK, the British Standards Institution (BSI) has been commissioned by the UK Department of Business, Innovation and Skills (BIS) to conceive a Smart Cities Standards Strategy to identify vectors of smart city development where standards are needed. The standards would be developed through a consensus-driven process under the BSI to ensure good practise is shared between all the actors. The BIS launched the City's Standards Institute to bring together cities and key industry leaders and innovators to work together in identifying the challenges facing cities, providing solutions to common problems and defining the future of smart city standards [35].

PAS 181 Smart city framework- Guide to establishing strategies for smart cities and communities establishes a good practice framework for city leaders to develop, agree and deliver smart city strategies that can help transform their city’s ability to meet challenges faced in the future and meet the goals. The smart city framework (SCF) does not intend to describe a one-size-fits-all model for the future of UK cities but focuses on the enabling processes by which the innovative use of technology and data, together with organizational change, can help deliver the diverse visions for future UK cities in more efficient, effective and sustainable ways [36].
PD 8101 Smart cities- Guide to the role of the planning and development process gives guidance regarding planning for new development for smart city plans and provides an overview of the key issues to be considered and prioritized. The document is for use by local authority planning and regeneration officers to identify good practice in a UK context, and what tools they could use to implement this good practice. This aims to enable new developments to be built in a way that will support smart city aspirations at minimal cost [37].
PAS 182 Smart city concept model. Guide to establishing a model for data establishes an interoperability framework and data-sharing between agencies for smart cities for the following purposes:
1. To have a city where information can be shared and understood between organizations and people at each level
2. The derivation of data in each layer can be linked back to data in the previous layer
3. The impact of a decision can be observed back in operational data. The smart city concept model (SCCM) provides a framework that can normalize and classify information from many sources so that data sets can be discovered and combined to gain a better picture of the needs and behaviours of a city’s citizens (residents and businesses) to help identify issues and devise solutions. PAS 182 is aimed at organizations that provide services to communities in cities, and manage the resulting data, as well as decision-makers and policy developers in cities [38].
PAS 180 Smart cities Vocabulary helps build a strong foundation for future standardization and good practices by providing an industry-agreed understanding of smart city terms and definitions to be used in the UK. It provides a working definition of a Smart City- “Smart Cities” is a term denoting the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens [39]. This aims to help improve communication and understanding of smart cities by providing a common language for developers, designers, manufacturers and clients. The standard also defines smart city concepts across different infrastructure and systems’ elements used across all service delivery channels and is intended for city authorities and planners, buyers of smart city services and solutions [40], as well as product and service providers.

Endnotes

[1] See: http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf.

[2] See: http://www.ibm.com/smarterplanet/in/en/sustainable_cities/ideas/.

[3] See: http://www.thehindubusinessline.com/economy/smart-cities-mission-welcome-to-tomorrows-world/article8163690.ece.

[4] See: http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf.

[5] See: http://www.iso.org/iso/news.htm?refid=Ref2042.

[6] See: http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html.

[7] See: http://www.nasscom.in/integrated-ict-and-geospatial-technologies-framework-100-smart-cities-mission.

[8] See: http://www.cxotoday.com/story/nasscom-creates-framework-for-smart-cities-project/.

[9] See: http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html.

[10] See: http://www.business-standard.com/article/economy-policy/in-a-first-bis-to-come-up-with-standards-for-smart-cities-115060400931_1.html.

[11] See: http://www.longfinance.net/groups7/viewdiscussion/72-financing-financing-tomorrow-s-cities-how-standards-can-support-the-development-of-smart-cities.html?groupid=3.

[12] See: http://www.iso.org/iso/iso_technical_committee?commid=656906.

[13] See: http://cityminded.org/wp-content/uploads/2014/11/Patricia_McCarney_PDF.pdf.

[14] See: http://www.iso.org/iso/news.htm?refid=Ref1877.

[15] See: http://smartcitiescouncil.com/article/new-iso-standard-gives-cities-common-performance-yardstick.

[16] See: http://smartcitiescouncil.com/article/dissecting-iso-37120-why-new-smart-city-standard-good-news-cities.

[17] See: http://www.iso.org/iso/catalogue_detail?csnumber=62436.

[18] See: http://www.cityindicators.org/.

[19] See: http://www.dataforcities.org/.

[20] See: http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html.

[21] See: http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html.

[22] See: http://www.iso.org/iso/37120_briefing_note.pdf.

[23] See: http://www.dataforcities.org/wccd/.

[24] See: http://resilient-cities.iclei.org/fileadmin/sites/resilient-cities/files/Webinar_Series/HERNANDEZ_-_ICLEI_Resilient_Cities_Webinar__FINAL_.pdf.

[25] See: http://www.iso.org/iso/iso_technical_committee?commid=656967.

[26] See: https://www.iso.org/obp/ui/#iso:std:iso:ts:37151:ed-1:v1:en.

[27] See: http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref2001&utm_medium=email&utm_campaign=ISO+Newsletter+November&utm_content=ISO+Newsletter+November+CID_4182720c31ca2e71fa93d7c1f1e66e2f&utm_source=Email%20marketing%20software&utm_term=Read%20more.

[28] See: http://www.iso.org/iso/37120_briefing_note.pdf.

[29] See: http://standardsforum.com/isots-37151-smart-cities-metrics/.

[30] See: http://www.iso.org/iso/executive_summary_iso_37150.pdf.

[31] See: http://standardsforum.com/isots-37151-smart-cities-metrics/.

[32] See: http://cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards.

[33] See: http://smartcitiescouncil.com/article/itu-takes-internet-things-standards-smart-cities.

[34] See: https://www.itu.int/net/pressoffice/press_releases/2015/22.aspx.

[35] See: http://www.bsigroup.com/en-GB/smart-cities/.

[36] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-181-smart-cities-framework/.

[37] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PD-8101-smart-cities-planning-guidelines/.

[38] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-182-smart-cities-data-concept-model/.

[39] See: http://www.iso.org/iso/smart_cities_report-jtc1.pdf.

[40] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-180-smart-cities-terminology/.

For more details visit https://cis-india.org/internet-governance/blog/adoption-of-standards-in-smart-cities-way-forward-for-india

Exploring Big Data for Development: An Electricity Sector Case Study from India

sumandro — 2019-03-16T04:33:15Z

This working paper by Ritam Sengupta, Dr. Richard Heeks, Sumandro Chattapadhyay, and Dr. Christopher Foster draws from the field study undertaken by Ritam Sengupta, and is published by the Global Development Institute, University of Manchester. The field study was commissioned by the CIS, with support from the University of Manchester and the University of Sheffield.

Download the working paper: PDF

Abstract

This paper presents exploratory research into “data-intensive development” that seeks to inductively identify issues and conceptual frameworks of relevance to big data in developing countries. It presents a case study of big data innovations in “Stelcorp”; a state electricity corporation in India. In an attempt to address losses in electricity distribution, Stelcorp has introduced new digital meters throughout the distribution network to capture big data, and organisation-wide information systems that store and process and disseminate big data.

Emergent issues are identified across three domains: implementation, value and outcome. Implementation of big data has worked relatively well but technical and human challenges remain. The advent of big data has enabled some – albeit constrained – value addition in all areas of organisational operation: customer billing, fault and loss detection, performance measurement, and planning. Yet US$ tens of millions of investment in big data has brought no aggregate improvement in distribution losses or revenue collection. This can be explained by the wider outcome, with big data faltering in the face of external politics; in this case the electoral politics of electrification. Alongside this reproduction of power, the paper also reflects on the way in which big data has enabled shifts in the locus of power: from public to private sector; from labour to management; and from lower to higher levels of management.

A number of conceptual frameworks emerge as having analytical power in studying big data and global development. The information value chain model helps track both implementation and value-creation of big data projects. The design-reality gap model can be used to analyse the nature and extent of barriers facing big data projects in developing countries. And models of power – resource dependency, epistemic models, and wider frameworks – are all shown as helping understand the politics of big data.

Cross-posted from University of Manchester.

For more details visit https://cis-india.org/raw/exploring-big-data-for-development-an-electricity-sector-case-study-from-india

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

sumandro — 2016-04-28T07:28:23Z

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

Study Process and Deliverable

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

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

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

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

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

Research Questions

The interviews will focus on the following topics:

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

Application, Eligibility, and Remuneration

Please submit the following documents to apply:

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

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

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

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

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

Big Data in the Global South International Workshop

praskrishna — 2015-11-06T02:04:49Z

Institute for Technology and Society of Rio de Janeiro welcomes you to an international workshop on Big Data at Hotel Windsor Florida, Rua Ferreira Viana, Flamengo, Rio de Janeiro, Brazil on November 16 and 17, 2015. Open Society Foundations and British Embassy Brasilia are sponsors for the event. The Centre for Internet & Society (CIS) is a research partner. Sunil Abraham, Pranesh Prakash and Vipul Kharbanda will be speaking at this event.

The event will bring together key representatives from government, civil society, the business sector and academia from Brazil, India, United Kingdom and several other countries. This is a closed multistakeholder round-table to discuss and map international examples of Big Data uses and regulation, both by private and public sectors, in order to develop practical strategies to promote adoption of harmonized rules by different actors. The event will also map existing initiatives involving the use of Big Data and present the results of a joint research initiative conducted by ITS and CIS in this field.

Resources

For more details visit https://cis-india.org/internet-governance/events/big-data-in-the-global-south-international-workshop

To be Counted When They Count You: Words of Caution for the Gender Data Revolution

noopur — 2022-02-01T01:06:08Z

In 2015, after the announcement of the SDGs or Sustainable Development Goals, a new global developmental framework through the year 2030, the United Nations described data as the “lifeblood of decision-making and the raw material for accountability” for the purpose of realizing these developmental goals. This curious yet key link between these new developmental goals and the use of quantitative data for agenda setting invited a flurry of big data-led initiatives such as but not limited to Data2X, that sought to further strengthen and solidify the relationship between ‘Big Development’ and ‘Big Data.’

One of those SDG goals (Goal 5) prioritizes gender equality and empowerment of women and girls not only as a standalone goal but also as a crucial factor to realizing the other goals. In response, several academic and non-profit initiatives have begun to interpret and conduct data-led gendered development or the “gender data revolution”. As with other data discourses, the gender-data discourse is also one of ‘speed’, charging ahead using a variety of quantitative and visualization approaches to reveal and eventually solve gendered problems of development.

These interventions also invite some classical critical questions: who is setting the agenda for the gender data revolution and who are its imagined subjects? How are questions of participation and asymmetries of power in developmental research being addressed? How does the gender data revolution address the situatedness as well as incompleteness of data records in the Global South (where most sites of intervention are)? Speaking specifically to the theme of this special issue (‘cross-cultural feminist technologies’), this paper demonstrates how the welfarist discourse of data-led gender development is, in fact, assembled through the overwhelming enumeration of female-identifying bodies in the Global South.

The paper offers critical historical insights from the fields of international development, anthropology, and postcolonial history to caution against both, the possible harms of gender disaggregated datafication as well as the consequences of non-participatory datafication of women, the subjects of the gender data revolution.

Read the full paper here.

This study was undertaken as part of the Big Data for Development network supported by the International Development Research Centre, Canada, and is shared under Creative Commons Attribution 4.0 International license.

The views and opinions expressed on this page are those of their individual authors. Unless the opposite is explicitly stated, or unless the opposite may be reasonably inferred, CIS does not subscribe to these views and opinions which belong to their individual authors. CIS does not accept any responsibility, legal or otherwise, for the views and opinions of these individual authors. For an official statement from CIS on a particular issue, please contact us directly.

For more details visit https://cis-india.org/raw/to-be-counted-when-they-count-you-words-of-caution-for-the-gender-data-revolution

Big Data for governance

Admin — 2017-11-08T01:42:18Z

Recent times have witnessed an explosion of data as users started leaving a huge data footprint everywhere they go. Interestingly, this period has seen a phenomenal increase in computing power couple by a drop in costs of storage.

The article by Alekhya Hanumanthu was published in Telangana Today on November 4, 2017.

India is now sitting on the data so generated and subjecting it to data analytics for uses in various sectors like insurance, education, healthcare, governance, so on and so forth.

According to Centre for Internet and Society (CIS), in 2015, the Government of Narendra Modi launched Digital India Programme to ensure availability of government services to citizens electronically by improving online infrastructure and Internet connectivity.

Amongst other things, e-Governance and e-Kranti intend to reform governance through technology and enable electronic delivery of services. Needless to say, it will involve large scale digitisation, electronic collection of data from residents and processing. The Big data so created will help policy making evolve into a data backed, action oriented initiative with accountability asserted where it is due.

Let’s take a look at some Big Data based initiatives underway according to analyticsindiamag:

Project insight: Undertaken up by Indian tax agencies, Project Insight is an advanced analytical tool that is a comprehensive platform that encourages compliance of tax while at the same time it prevents non-compliance. Significantly, it will be used to detect fraud, support investigations and provide insights for policy making. For instance, it will detect the social media activity of a person to glean their spending and check if it is commensurate with the tax they have paid during that year. Needless to say, this will also unearth sources of black money.

Economic Development Board in Andhra: CORE-CM Office Realtime Executive Dashboard is an integrated dashboard established to monitor category-wise key performance indicators of various departments/schemes in real time. Users can check key performance indicators of various departments, schemes, initiatives, programmes, etc. With a panoply of services information ranging from Women and Child Welfare to Street lights monitoring, it has become an exemplary role model of governance.

Geo-tagging of assets under Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA): Under the guidance of Narendra Modi, online monitoring of assets to check leakages Ministry of Rural development was started. To achieve this, they were tied up with ISRO and National Informatics Centre to geo tag MGNREGA assets. According to India Today, the assets created range from plantations, rural infrastructure, water harvesting structures, flood control measures such as check dams etc. To do this, a junior engineer takes a photo of an asset and uploads it on the Bhuvan web portal run by ISRO’s National Remote Sensing Centre via a mobile app. Once a photo is uploaded, time and location gets encrypted automatically. Thus, the Government hopes to hold an ironclad control of the resources thus disseminated.

CAG’s centre for Data Management and Analytics: According to Comptroller and Auditor General of India, The CAG’s Centre for Data Management and Analytics (CDMA) is going to play a catalytic role to synthesise and integrate relevant data into auditing process. According to an announcement on National Informatics Centre (NIC), it aims to build up capacity in the Indian Audit and Accounts Department in Big Data Analytics to explore the data rich environment at the Union and State levels. What’s more, this initiative of CAG of India, puts it amongst the pioneers in institutionalising data analytics in government audit in the international community.

Task Force to spruce up Employment Data: The data provided by Labour Bureau is limited and not timely enough for policymakers to assess the need for job creation. To address this gap, the Government has set up a committee tasked to fill the employment data gap and ensure the timely availability of reliable information regarding job creation. Thus the top line of Government has a direct view of where the employment gaps are so that it can facilitate creation of appropriate jobs.

What’s the big picture?

Policy making and governance by Indian government have traditionally been rife with red tape, bureaucracy and corruption. Lack of accountability on part of Government workforce not only impacted the quantity and quality of work delivered but also invited corrupt practices and leakages. So, Big data is a welcome change in direction.

For more details visit https://cis-india.org/internet-governance/news/telangana-today-november-8-2017-alekhya-hanumanthu-big-data-for-governance

#NAMAprivacy: Data standards for IoT and home automation systems

Admin — 2017-11-08T02:15:52Z

On 5th October, MediaNama held a #NAMAprivacy conference in Bangalore focused on Privacy in the context of Artificial Intelligence, Internet of Things (IoT) and the issue of consent, supported by Google, Amazon, Mozilla, ISOC, E2E Networks and Info Edge, with community partners HasGeek and Takshashila Institution. Part 1 of the notes from the discussion on IoT:

Link to the original published by Medianama on October 18 here

The second session of the #NAMAprivacy in Bangalore dealt with the data privacy in the Internet of Things (IoT) framework. All three panelists for the session – Kiran Jonnalagadda from HasGeek, Vinayak Hegde, a big data consultant working with ZoomCar and Rohini Lakshane a policy researcher from CIS – said that they were scared about the spread of IoT at the moment. This led to a discussion on the standards which will apply to IoT, still nascent at this stage, and how it could include privacy as well.

Hedge, a volunteer with the Internet Engineering Task Force (IETF) which was instrumental in developing internet protocols and standards such as DNS, TCP/IP and HTTP, said that IETF took a political stand recently when it came to privacy. “One of the discussions in the IETF was whether security is really important? For a long time, the pendulum swung the other way and said that it’s important and that it’s not big enough a trade-off until the bomb dropped with the Snowden revelations. The IETF has always avoided taking any political stance. But for the first time, they did take a political position and they published a request for comments which said: “Pervasive monitoring is an attack on the Internet” and that has become a guiding standard for developing the standards,” he explained.

He added that this led the development of new standards which took privacy into consideration by default.

“The repercussions has been pervasive across all the layers of the stack whether it is DNS and the development of DNS Sec. The next version of HTTP, does not actually mandate encryption but if you look at all the implementation on the browser side, all of them without exception have incorporated encryption,” he added.

Rohini added that discussion around the upcoming 5G standard, where large-scale IoT will be deployed, also included increased emphasis on privacy. “It is essentially a lot of devices connected to the Internet and talking to each other and the user. The standards for security and privacy for 5G are being built and some of them are in the process of discussion. Different standard-setting bodies have been working on them and there is a race of sorts for setting them up by stakeholders, technology companies, etc to get their tech into the standard,” she said.

“The good thing about those is that they will have time to get security and privacy. Here, I would like to mention RERUM which is formed from a mix of letters which stands for Reliable, Resilient, and Secure IoT for smart cities being piloted in the EU. It essentially believes that security should include reliability and privacy by design. This pilot project was thought to allow IoT applications to consider security and privacy mechanisms early in the design, so that they could balance reliability. Because once a standard is out or a mechanism is out, and you implement something as large as a smart city, it is very difficult to retrofit these considerations,” she explained.

Privacy issues in home automation and IoT

Rohini pointed out a report which illustrates the staggering amount of data collection which will be generated by home automation. “I was looking for figures, and I found an FTC report published in 2015 where one IoT company revealed in a workshop that it provides home automation to less than 10,000 households but all of them put together account for 150 million data points per day. So that’s one data point for every six seconds per household. So this is IoT for home automation and there is IoT for health and fitness, medical devices, IoT for personal safety, public transport, environment, connected cars, etc.”

In this sort of situation, the data collected could be used for harms that users did not account for.

“I received some data a couple of years back and the data was from a water flowmeter. It was fitted to a villa in Hoskote and the idea was simple where you could measure the water consumption in the villa and track the consumption. So when I received the data, I figured out by just looking at the water consumption, you can see how many people are in the house, when they get up at night, when they go out, when they are out of station. All of this data can be misused. Data is collected specifically for water consumption and find if there are any leakages in the house. But it could be used for other purposes,” Arvind P from Devopedia said.

Pranesh Prakash, policy director at Centre for Internet and Society (CIS), also provided an example of a Twitter handle called “should I be robbed now” where it correlates a user’s vacation pictures says that they could be robbed. “What we need to remember is that a lot of correlation analysis is not just about the analysis but it is also about the use and misuse of it. A lot of that use and misuse is non-transparent. Not a single company tells you how they use your data, but do take rights on taking your data,” he added.

Vinayak Hedge also added that the governments are using similar methods of data tracking to catch bitcoin miners in China and Venezuela from smart meters.

“In China, there are all these bitcoin miners. I was reading this story in Venezuela, where bitcoin mining is outlawed. The way they’re catching these bitcoin miners is by looking at their electricity consumption. Bitcoin mining uses a huge amount of power and computing capacity. And people have come out with ingenious ways of getting around it. They will draw power from their neighbours or maybe from an industrial setting. This could be a good example for a privacy-infringing activity.”

Pseudonymization

Srinivas P, head of security at Infosys, pointed out that a possible solution to provide privacy in home automation systems could be the concept of pseudonymity. Pseudonymization is a procedure by which the most identifying fields within a data record are replaced by one or more artificial identifiers or pseudonyms.

“There are a number of home automation systems which are similar to NEST, which is extensively used in Silicon Valley homes, that connect to various systems. For example, when you are approaching home, it will know when to switch on your heating system or AC based on the weather. And it also has information on who stays in the house and what room and what time they sleep. And in a the car, it gives a full real-time profile about the situation at home. It can be a threat if it is hacked. This is a very common threat that is being talked about and how to introduce pseudo-anonymity. When we use these identifiers, and when the connectivity happens, how do we do so that the name and user are not there? Pseudonymity can be introduced so that it becomes difficult for the hacker to decipher who this guy is,” Srinivas added.

Ambient data collection

With IoT, it has never been able to capture ambient data. Ambient data is information that lies in areas not generally accessible to the user. An example for this is how users get traffic data from Internet companies. Kiran Jonnalagadda explained how this works:

“When you look at traffic data on a street map, where is that data coming from? It’s not coming from the fact that there is an app on the phone constantly transmitting data from the phone. It’s coming from the fact that cell phone towers record who is coming to them and you know if the cell phone tower is facing the road, and it has so many connections on it, you know that traffic is at a certain level in that area. Now as a user of the map, you are talking to a company which produces this map and it is not a telecom company. Someone who is using a phone is only dealing with a telecom company and how does this data transfer happen and how much user data is being passed on to the last mile user who is actually holding the phone.”

Jonnalagadda stressed on the need for people to ask who is aggregating this ambient data.

“Now obviously, when you look at the map, you don’t get to see, who is around you. And that would be a clear privacy violation and you only get to see the fact that traffic is at a certain level of density around the street around you. But at what point is the aggregation of data happening from an individually identifiable phone to just a red line or a green line indicating the traffic in an area. We also need to ask who is doing this aggregation. Is it happening on the telecom level? Is it happening on the map person level and what kind of algorithms are required that a particular phone on a cell phone network represents a moving vehicle or a pedestrian? Can a cell phone company do that or does a map company do that? If you start digging and see at what point is your data being anonymized and who is responsible for anonmyzing it and you think that this is the entity that is supposed to be doing it, we start realizing that it is a lot more complicated and a lot more pervasive than we thought it would be,” he said.

#NAMAprivacy Bangalore:

Will artificial Intelligence and Machine Learning kill privacy? [read]
Regulating Artificial Intelligence algorithms [read]
Data standards for IoT and home automation systems [read]
The economics and business models of IoT and other issues [read]

#NAMAprivacy Delhi:

Blockchains and the role of differential privacy [read]
Setting up purpose limitation for data collected by companies [read]
The role of app ecosystems and nature of permissions in data collection [read]
Rights-based approach vs rules-based approach to data collection [read]
Data colonisation and regulating cross border data flows [read]
Challenges with consent; the Right to Privacy judgment [read]
Consent and the need for a data protection regulator [read]
Making consent work in India [read]

For more details visit https://cis-india.org/internet-governance/news/medianama-october-18-2017-namaprivacy-data-standards-for-iot

OPINION | Data is New Oil and Human Mind the New Battlefield. India Must Wake Up Now

Admin — 2017-11-26T03:28:55Z

In information warfare, the battlespace is the human mind. This is where the privacy of an individual intersects with national security. Fighting this battle will require a new paradigm in thought and action.

The article by Lt. General (Retd.) D. S. Hooda was published by News18.com on November 11, 2017

A few days ago, the Army Headquarters took out a public advisory warning about a “deliberate misinformation campaign being launched by vested interests some of which is being initiated from countries bordering our nation.” This is an acknowledgment of the use of social media for what is today considered the most dominant form of warfare — ‘information warfare’. It has been extensively used by our adversaries in Jammu and Kashmir to show the government and security forces in poor light.

Deception, propaganda and misinformation have always been a part of warfare but what is different today is that the tools of information warfare have acquired a new dimension. An integration of massive amounts of data with Artificial Intelligence (AI) has given a significant weapon in the hands of information warriors.

The cost of saving data has been plummeting, with the cost being halved about every 15 months. Now more and more data about individuals is being saved, both by corporations and governments. In his book, Data and Goliath, Bruce Schneier writes that worldwide, Google has the capacity to store 15 exabytes of data. To put it in context, one exabyte is 500 billion pages of text. Bruce also quotes the case of Max Schrems, an Austrian law student, who in 2011 demanded all his personal data from Facebook. After a two year legal battle, Facebook gave him a CD with 1200 pages of PDF. This is how much Facebook knows about you, and it does not forget because it is all saved.

All this big data would be useless unless it can be utilised for decision making and this is where advances in AI have provided the breakthrough. Smart machines mine the data and detect trends, patterns, habits, ideology and desires. These personal characteristics of individuals are being used by corporations to send targeted advertisements to influence commercial decisions.

The same technique is used in information warfare. On November 1, the US House Intelligence Committee released Facebook advertisements bought by Russian operatives to influence the 2016 elections. Washington Post wrote, “The ads made visceral appeals to voters concerned about illegal immigration...African American political activism, rising prominence of Muslims” among other issues. Senator Angus King said, “The strategy is to take a crack in our society and turn it into a chasm.”

Data is the new oil and that is exactly how it is being traded and sold. In the absence of any legal provisions, companies and ‘data brokers’ are sharing and selling personal data. Can this personal data find its way to a hostile government? Last month, the US Army brought out that their troops in the Baltic had reported instances of cell phone hacking. However, more worrisome was the fact the hackers knew personal details of the soldiers. Direct threats against family members of the military can have a negative psychological impact during conflict.

India has its share of political, social and ethnic differences, just as in many societies. In recent times these differences have been magnified as nationalism has taken centre stage. It is difficult to imagine why these fault lines will not be exploited by inimical forces as India enters the election mode in 2018. Looking at examples from the US and French elections, Brexit and the cyber battle during the Catalonia referendum, I think we have no option but to be prepared.

The preparation for this war (and I do not use this word lightly) lies in three spheres — concepts, practices and structures.

Conceptually, our current shortcoming is that we are viewing this issue through a technical prism rather than the broader spectrum of information warfare. CERT and NTRO can technically protect our critical infrastructure but they do not have an equal understanding of the human dimension, which is more strategic than scientific. The Americans, world leaders in information technology, have not been able to prevent a perceived subversion of their democratic process.

Our practices need to improve. The security of personal data is a major concern. The Supreme Court has declared privacy as a fundamental right, but there are no privacy laws to back it up. Even data stored in India is not safe as the owners of our data are the giant technology companies, mostly based in the US and not under our legal control. In September 2017, it was reported that Google has quietly stopped challenging most search warrants from US judges in which the data requested is stored on overseas servers.

A May 2017, report by the Centre for Internet and Society estimated that 135 million Aadhaar numbers could have been leaked from official portals. This was not due to a security breach but due to poor privacy practices.

Our continued reliance on foreign hardware and software is extremely worrisome. There was clear evidence that Cisco systems had been back-doored by the American National Security Agency but the Indian military continues to procure hardware from Cisco. There is a similar story with Chinese equipment in our telecommunication and power sectors. An attempt to introduce an Indian operating system to replace Windows in the Army has been mired in controversy.

In case of a targeted cyber attack on India, there is little we can do except issue advisories. The solutions will have to come from foreign manufactures or developers whose equipment we are using. There is an urgent need to give a fillip to developing indigenous solutions for our critical infrastructure.

And finally, structures. An organisation to execute information warfare would have to be led by the Ministry of Defence, because the threat is mainly from external players. It would be a combination of military planners, specialists from the field of intelligence, government agencies, media and cyber warfare experts. Such an organisation does not currently exist, though the raising of the Cyber Command could fill this gap.

In information warfare, the battlespace is the human mind. This is where the privacy of an individual intersects with national security. Fighting this battle will require a new paradigm in thought and action.

(The author is former Northern Commander, Indian Army, under whose leadership India carried out surgical strikes against Pakistan in 2016. Views are personal.)

For more details visit https://cis-india.org/internet-governance/news/news-18-lt-general-retd-ds-hooda-data-is-new-oil-and-human-mind-the-new-battlefield-india-must-wake-up-now

Identity of the Aadhaar Act: Supreme Court and the Money Bill Question

Vanya Rakesh and Sumandro Chattapadhyay — 2016-05-09T11:52:44Z

A writ petition has been filed by former Union minister Jairam Ramesh on April 6 challenging the constitutionality and legality of the treatment of this Act as a money bill. The Supreme Court heard the matter on April 25 and invited the Union government to present its view. It is our view that the Supreme Court can not only review the Lok Sabha speaker’s decision, but should also ask the government to draft the Aadhaar Bill again, this time with greater parliamentary and public deliberation. Vanya Rakesh and Sumandro Chattapadhyay wrote this article on The Wire.

Published by and cross-posted from The Wire.

The Aadhaar Act 2016, passed in the Lok Sabha on March 16, 2016, faced opposition ever since it was tabled in parliament. In particular, the move to introduce it as a money bill has been vehemently challenged on grounds of this being an attempt to bypass the Rajya Sabha completely. A writ petition has been filed by former Union minister Jairam Ramesh on April 6 challenging the constitutionality and legality of the treatment of this Act as a money bill. The Supreme Court heard the matter on April 25 and invited the Union government to present its view.

It is our view that the Supreme Court can not only review the Lok Sabha speaker’s decision, but should also ask the government to draft the Aadhaar Bill again, this time with greater parliamentary and public deliberation.

The money bill question

M.R. Madhavan has argued that the Aadhaar Act contains matters other than “only” those incidental to expenditure from the consolidated fund, as it establishes a biometrics-based unique identification number for beneficiaries of government services and benefits, but also allows the number to be used for other purposes beyond service delivery. While Pratap Bhanu Mehta calls this a subversion of “the spirit of the constitution”, P.D.T. Achary, former secretary general of the Lok Sabha, expressed concern about the attempts to pass off financial bills like Aadhaar as money bills as a means to circumvent and erode the supervisory role of the Rajya Sabha. Arvind Datar has further emphasised that when the primary purpose of a bill is not governed by Article 110(1), then certifying it as a money bill is an unconstitutional act.

Article 110(1) of the Constitution identifies a bill as a money bill if it contains “only” provisions dealing with the following matters, or those incidental to them:

imposition and regulation of any tax,
financial obligations undertaken by Indian Government,
payment into or withdrawal from the Consolidated Fund of India (CFI) or Contingent Fund of India,
appropriation of money and expenditure charged on the CFI or receipt, and
custody, issue or audit of money into CFI or public account of India.

However, the link of the Act with the Consolidated Fund of India is rather tenuous, since it depends on the Union or state governments declaring a certain subsidy to be available upon verification of the Aadhaar number. The objectives and validity of the Act would not actually change if the Aadhaar number no longer was directly connected to the delivery of services. The use of the word “if” in section 7 explicitly leaves scope for a situation where the government does not declare an Aadhaar verification as necessary for accessing a subsidy. In such a scenario, the Act will still be valid but without any formal connection with any charges on the Consolidated Fund of India.

A case of procedural irregularity?

The constitution of India borrows the idea of providing the speaker with the authority to certify a bill as money bill from British law, but operationalises it differently. In the UK, though the speaker’s certificate on a money bill is conclusive for all purposes under section 3 of the Parliament Act 1911, the speaker is required to consult two senior members, usually one from either side of the house, appointed by the committee from amongst those senior MPs who chair general committees. In India, the speaker makes the decision on her own.

Although article 110 (3) of the Indian constitution states that the decision of the speaker of the Lok Sabha shall be final in case a question arises regarding whether a bill is a money bill or not, this does not restrict the Supreme Court from entertaining and hearing a petition contesting the speaker’s decision. As the Aadhaar Act was introduced in the Lok Sabha as a money bill even though it does not meet the necessary criteria for such a classification, this treatment of the bill may be considered as an instance of procedural irregularity.

There is ample jurisprudence on what happens when the Supreme Court’s power of judicial review comes up against Article 122 – which states that the validity of any proceeding in the parliament can (only) be called into question on the grounds of procedural irregularities. In the crucial judgment of Raja Ram Pal vs Hon’ble Speaker, Lok Sabha and Others (2007), the court evaluated the scope of judicial review and observed that although parliament is supreme, unlike Britain, proceedings which are found to suffer from substantive illegality or unconstitutionality, cannot be held protected from judicial scrutiny by article 122, as opposed to mere irregularity. Deciding upon the scope for judicial intervention in respect of exercise of power by the speaker, in Kihoto Hollohan vs Zachillhu and Ors. (1992), the Supreme Court held that though the speaker of the house holds a pivotal position in a parliamentary democracy, the decision of the speaker (while adjudicating on disputed disqualification) is subject to judicial review that may look into the correctness of the decision.

Several past decisions of the Supreme Court discuss how the tests of legality and constitutionality help decide whether parliamentary proceedings are immune from judicial review or not. In Ramdas Athawale vs Union of India (2010), the case of Keshav Singh vs Speaker, Legislative Assembly (1964) was referred to, in which the judges had unequivocally upheld the judiciary’s power to scrutinise the actions of the speaker and the houses. It was observed that if the parliamentary procedure is illegal and unconstitutional, it would be open to scrutiny in a court of law and could be a ground for interference by courts under Article 32, though the immunity from judicial interference under this article is confined to matters of irregularity of procedure. These observations were reiterated in Mohd. Saeed Siddiqui vs State of Uttar Pradesh (2014) and Yogendra Kumar Jaiswal vs State of Bihar (2016).

Thus, the decision of the Lok Sabha speaker to pass and certify a bill as a money bill is definitely not immune from judicial review. Additionally, the Supreme Court has the power to issue directions, orders or writs for enforcement of rights under Article 32 of the constitution, therefore, allowing the judiciary to decide upon the manner of introducing the Aadhaar Act in parliament.

National implications demand public deliberation

As the provisions of the Aadhaar Act have far reaching implications for the fundamental and constitutional rights of Indian citizens, the Supreme Court should look into the matter of its identification and treatment as a money bill and whether such decisions lead to the thwarting of legislative and procedural justice.

The Supreme Court may also take this opportunity to reflect on the very decision making process for classification of bills in general. As Smarika Kumar argues, experience with the Aadhaar Act reveals a structural concern regarding this classification process, which may have substantial implications in terms of undermining public and parliamentary deliberative processes. This “trend,” as Arvind Datar notes, of limiting legislative discussions and decisions of national importance within the space of the Lok Sabha must be swiftly curtailed.

Apart from deciding upon the legality of the nature of the bill, it is vital that the apex court ask the government to categorically respond to the concerns red-flagged by the Standing Committee on Finance, which had taken great exception to the continued collection of data and issuance of Aadhaar numbers in its report, and to the recommendations passed in the Rajya Sabha recently. Further, the repeated violation of the Supreme Court’s interim orders – that the Aadhaar number cannot be made mandatory for availing benefits and services – in contexts ranging from marriages to the guaranteed work programme should also be addressed and responses sought from the Union government.

Evidently, the substantial implications of the Aadhaar Act for national security and fundamental rights of citizens, primarily privacy and data security, make it imperative to conduct a duly balanced public deliberation process, both within and outside the houses of parliament, before enacting such a legislation.

For more details visit https://cis-india.org/internet-governance/blog/identity-of-the-aadhaar-act-supreme-court-and-the-money-bill-question

Sean McDonald - Ebola: A Big Data Disaster

sumandro — 2016-04-21T09:57:26Z

We are proud to initiate the CIS Papers series with a fascinating exploration of humanitarian use of big data and its discontents by Sean McDonald, FrontlineSMS, in the context of utilisation of Call Detail Records for public health response during the Ebola crisis in Liberia. The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modeling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.

Read

Download the paper: PDF.

Preface

This study titled “Ebola: A Big Data Disaster” by Sean Martin McDonald, undertaken with support from the Open Society Foundation, Ford Foundation, and Media Democracy Fund, explores the use of Big Data in the form of Call Detail Record (CDR) data in humanitarian crisis.

It discusses the challenges of digital humanitarian coordination in health emergencies like the Ebola outbreak in West Africa, and the marked tension in the debate around experimentation with humanitarian technologies and the impact on privacy. McDonald’s research focuses on the two primary legal and human rights frameworks, privacy and property, to question the impact of unregulated use of CDR’s on human rights. It also highlights how the diffusion of data science to the realm of international development constitutes a genuine opportunity to bring powerful new tools to fight crisis and emergencies.

Analysing the risks of using CDRs to perform migration analysis and contact tracing without user consent, as well as the application of big data to disease surveillance is an important entry point into the debate around use of Big Data for development and humanitarian aid. The paper also raises crucial questions of legal significance about the access to information, the limitation of data sharing, and the concept of proportionality in privacy invasion in the public good. These issues hold great relevance in today's time where big data and its emerging role for development, involving its actual and potential uses as well as harms is under consideration across the world.

The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modelling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.

This study offers an important perspective for us at the Centre for Internet and Society, and our works on Privacy, Big Data, and Big Data for Development, and very productively articulates the risks of adopting solutions to issues important for development without taking into consideration legal implications and the larger impact on human rights. We look forward to continue to critically engage with issues raised by Big Data in the context of human rights and sustainable development, and bring together diverse perspectives on these issues.

- Elonnai Hickok, Policy Director, the Centre for Internet and Society

CIS Papers

The CIS Papers series publishes open access monographs and discussion pieces that critically contribute to the debates on digital technologies and society. It includes publication of new findings and observations, of work-in-progress, and of critical review of existing materials. These may be authored by researchers at or affiliated to CIS, by external researchers and practitioners, or by a group of discussants. CIS offers editorial support to the selected monographs and discussion pieces. The views expressed, however, are of the authors' alone.

For more details visit https://cis-india.org/papers/ebola-a-big-data-disaster

Aadhaar Bill 2016 & NIAI Bill 2010 - Comparing the Texts

sumandro — 2016-03-09T11:25:01Z

This is a quick comparison of the texts of the Aadhaar Bill 2016 and the National Identification Authority of India Bill 2010. The new sections in the former are highlighed, and the deleted sections (that were part of the latter) are struck out.

Source: http://cis-india.github.io/aadhaar-bill-2016/

For more details visit https://cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison

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

Summary Report Internet Governance Forum 2015

jyoti — 2015-11-30T10:47:13Z

Centre for Internet and Society (CIS), India participated in the Internet Governance Forum (IGF) held at Poeta Ronaldo Cunha Lima Conference Center, Joao Pessoa in Brazil from 10 November 2015 to 13 November 2015. The theme of IGF 2015 was ‘Evolution of Internet Governance: Empowering Sustainable Development’. Sunil Abraham, Pranesh Prakash & Jyoti Panday from CIS actively engaged and made substantive contributions to several key issues affecting internet governance at the IGF 2015. The issue-wise detail of their engagement is set out below.

INTERNET GOVERNANCE

I. The Multi-stakeholder Advisory Group to the IGF organised a discussion on Sustainable Development Goals (SDGs) and Internet Economy at the Main Meeting Hall from 9:00 am to 12:30 pm on 11 November, 2015. The discussions at this session focused on the importance of Internet Economy enabling policies and eco-system for the fulfilment of different SDGs. Several concerns relating to internet entrepreneurship, effective ICT capacity building, protection of intellectual property within and across borders were availability of local applications and content were addressed. The panel also discussed the need to identify SDGs where internet based technologies could make the most effective contribution. Sunil Abraham contributed to the panel discussions by addressing the issue of development and promotion of local content and applications. List of speakers included:

Lenni Montiel, Assistant-Secretary-General for Development, United Nations
Helani Galpaya, CEO LIRNEasia
Sergio Quiroga da Cunha, Head of Latin America, Ericsson
Raúl L. Katz, Adjunct Professor, Division of Finance and Economics, Columbia Institute of Tele-information
Jimson Olufuye, Chairman, Africa ICT Alliance (AfICTA)
Lydia Brito, Director of the Office in Montevideo, UNESCO
H.E. Rudiantara, Minister of Communication & Information Technology, Indonesia
Daniel Sepulveda, Deputy Assistant Secretary, U.S. Coordinator for International and Communications Policy at the U.S. Department of State
Deputy Minister Department of Telecommunications and Postal Services for the republic of South Africa
Sunil Abraham, Executive Director, Centre for Internet and Society, India
H.E. Junaid Ahmed Palak, Information and Communication Technology Minister of Bangladesh
Jari Arkko, Chairman, IETF
Silvia Rabello, President, Rio Film Trade Association
Gary Fowlie, Head of Member State Relations & Intergovernmental Organizations, ITU

Detailed description of the workshop is available here http ://www .intgovforum .org /cms /igf 2015-main -sessions

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2327-2015-11-11-internet-economy-and-sustainable-development-main-meeting-room

Video link Internet economy and Sustainable Development here https://www.youtube.com/watch?v=D6obkLehVE8

II. Public Knowledge organised a workshop on The Benefits and Challenges of the Free Flow of Data at Workshop Room 5 from 11:00 am to 12:00 pm on 12 November, 2015. The discussions in the workshop focused on the benefits and challenges of the free flow of data and also the concerns relating to data flow restrictions including ways to address them. Sunil Abraham contributed to the panel discussions by addressing the issue of jurisdiction of data on the internet. The panel for the workshop included the following.

Vint Cerf, Google
Lawrence Strickling, U.S. Department of Commerce, NTIA
Richard Leaning, European Cyber Crime Centre (EC3), Europol
Marietje Schaake, European Parliament
Nasser Kettani, Microsoft
Sunil Abraham, CIS India

Detailed description of the workshop is available here http ://www .intgovforum .org /cms /workshops /list -of -published -workshop -proposals

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2467-2015-11-12-ws65-the-benefits-and-challenges-of-the-free-flow-of-data-workshop-room-5

Video link https://www.youtube.com/watch?v=KtjnHkOn7EQ

III. Article 19 and Privacy International organised a workshop on Encryption and Anonymity: Rights and Risks at Workshop Room 1 from 11:00 am to 12:30 pm on 12 November, 2015. The workshop fostered a discussion about the latest challenges to protection of anonymity and encryption and ways in which law enforcement demands could be met while ensuring that individuals still enjoyed strong encryption and unfettered access to anonymity tools. Pranesh Prakash contributed to the panel discussions by addressing concerns about existing south Asian regulatory framework on encryption and anonymity and emphasizing the need for pervasive encryption. The panel for this workshop included the following.

David Kaye, UN Special Rapporteur on Freedom of Expression
Juan Diego Castañeda, Fundación Karisma, Colombia
Edison Lanza, Organisation of American States Special Rapporteur
Pranesh Prakash, CIS India
Ted Hardie, Google
Elvana Thaci, Council of Europe
Professor Chris Marsden, Oxford Internet Institute
Alexandrine Pirlot de Corbion, Privacy International

Detailed description of the workshop is available here http ://www .intgovforum .org /cms /worksh o ps /list -of -published -workshop -proposals

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2407-2015-11-12-ws-155-encryption-and-anonymity-rights-and-risks-workshop-room-1

Video link available here https://www.youtube.com/watch?v=hUrBP4PsfJo

IV. Chalmers & Associates organised a session on A Dialogue on Zero Rating and Network Neutrality at the Main Meeting Hall from 2:00 pm to 4:00 pm on 12 November, 2015. The Dialogue provided access to expert insight on zero-rating and a full spectrum of diverse views on this issue. The Dialogue also explored alternative approaches to zero rating such as use of community networks. Pranesh Prakash provided a detailed explanation of harms and benefits related to different approaches to zero-rating. The panellists for this session were the following.

Jochai Ben-Avie, Senior Global Policy Manager, Mozilla, USA
Igor Vilas Boas de Freitas, Commissioner, ANATEL, Brazil
Dušan Caf, Chairman, Electronic Communications Council, Republic of Slovenia
Silvia Elaluf-Calderwood, Research Fellow, London School of Economics, UK/Peru
Belinda Exelby, Director, Institutional Relations, GSMA, UK
Helani Galpaya, CEO, LIRNEasia, Sri Lanka
Anka Kovacs, Director, Internet Democracy Project, India
Kevin Martin, VP, Mobile and Global Access Policy, Facebook, USA
Pranesh Prakash, Policy Director, CIS India
Steve Song, Founder, Village Telco, South Africa/Canada
Dhanaraj Thakur, Research Manager, Alliance for Affordable Internet, USA/West Indies
Christopher Yoo, Professor of Law, Communication, and Computer & Information Science, University of Pennsylvania, USA

Detailed description of the workshop is available here http://www.intgovforum.org/cms/igf2015-main-sessions

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2457-2015-11-12-a-dialogue-on-zero-rating-and-network-neutrality-main-meeting-hall-2

V. The Internet & Jurisdiction Project organised a workshop on Transnational Due Process: A Case Study in MS Cooperation at Workshop Room 4 from 11:00 am to 12:00 pm on 13 November, 2015. The workshop discussion focused on the challenges in developing an enforcement framework for the internet that guarantees transnational due process and legal interoperability. The discussion also focused on innovative approaches to multi-stakeholder cooperation such as issue-based networks, inter-sessional work methods and transnational policy standards. The panellists for this discussion were the following.

Anne Carblanc Head of Division, Directorate for Science, Technology and Industry, OECD
Eileen Donahoe Director Global Affairs, Human Rights Watch
Byron Holland President and CEO, CIRA (Canadian ccTLD)
Christopher Painter Coordinator for Cyber Issues, US Department of State
Sunil Abraham Executive Director, CIS India
Alice Munyua Lead dotAfrica Initiative and GAC representative, African Union Commission
Will Hudsen Senior Advisor for International Policy, Google
Dunja Mijatovic Representative on Freedom of the Media, OSCE
Thomas Fitschen Director for the United Nations, for International Cooperation against Terrorism and for Cyber Foreign Policy, German Federal Foreign Office
Hartmut Glaser Executive Secretary, Brazilian Internet Steering Committee
Matt Perault, Head of Policy Development Facebook

Detailed description of the workshop is available here http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2475-2015-11-13-ws-132-transnational-due-process-a-case-study-in-ms-cooperation-workshop-room-4

Video link Transnational Due Process: A Case Study in MS Cooperation available here https://www.youtube.com/watch?v=M9jVovhQhd0

VI. The Internet Governance Project organised a meeting of the Dynamic Coalition on Accountability of Internet Governance Venues at Workshop Room 2 from 14:00 – 15:30 on 12 November, 2015. The coalition brought together panelists to highlight the challenges in developing an accountability framework for internet governance venues that include setting up standards and developing a set of concrete criteria. Jyoti Panday provided the perspective of civil society on why acountability is necessary in internet governance processes and organizations. The panelists for this workshop included the following.

Robin Gross, IP Justice
Jeanette Hofmann, Director Alexander von Humboldt Institute for Internet and Society
Farzaneh Badiei, Internet Governance Project
Erika Mann, Managing Director Public PolicyPolicy Facebook and Board of Directors ICANN
Paul Wilson, APNIC
Izumi Okutani, Japan Network Information Center (JPNIC)
Keith Drazek , Verisign
Jyoti Panday, CIS
Jorge Cancio, GAC representative

Detailed description of the workshop is available here http://igf2015.sched.org/event/4c23/dynamic-coalition-on-accountability-of-internet-governance-venues?iframe=no&w=&sidebar=yes&bg=no

Video link https://www.youtube.com/watch?v=UIxyGhnch7w

VII. Digital Infrastructure Netherlands Foundation organized an open forum at Workshop Room 3 from 11:00 – 12:00 on 10 November, 2015. The open forum discussed the increase in government engagement with “the internet” to protect their citizens against crime and abuse and to protect economic interests and critical infrastructures. It brought together panelists topresent ideas about an agenda for the international protection of ‘the public core of the internet’ and to collect and discuss ideas for the formulation of norms and principles and for the identification of practical steps towards that goal. Pranesh Prakash participated in the e open forum. Other speakers included

Bastiaan Goslings AMS-IX, NL
Pranesh Prakash CIS, India
Marilia Maciel (FGV, Brasil
Dennis Broeders (NL Scientific Council for Government Policy)

Detailed description of the open forum is available here http://schd.ws/hosted_files/igf2015/3d/DINL_IGF_Open%20Forum_The_public_core_of_the_internet.pdf

Video link available here https://www.youtube.com/watch?v=joPQaMQasDQ

VIII. UNESCO, Council of Europe, Oxford University, Office of the High Commissioner on Human Rights, Google, Internet Society organised a workshop on hate speech and youth radicalisation at Room 9 on Thursday, November 12. UNESCO shared the initial outcome from its commissioned research on online hate speech including practical recommendations on combating against online hate speech through understanding the challenges, mobilizing civil society, lobbying private sectors and intermediaries and educating individuals with media and information literacy. The workshop also discussed how to help empower youth to address online radicalization and extremism, and realize their aspirations to contribute to a more peaceful and sustainable world. Sunil Abraham provided his inputs. Other speakers include

1. Chaired by Ms Lidia Brito, Director for UNESCO Office in Montevideo

2.Frank La Rue, Former Special Rapporteur on Freedom of Expression

3. Lillian Nalwoga, President ISOC Uganda and rep CIPESA, Technical community

4. Bridget O’Loughlin, CoE, IGO

5. Gabrielle Guillemin, Article 19

6. Iyad Kallas, Radio Souriali

7. Sunil Abraham executive director of Center for Internet and Society, Bangalore, India

8. Eve Salomon, global Chairman of the Regulatory Board of RICS

9. Javier Lesaca Esquiroz, University of Navarra

10. Representative GNI

11. Remote Moderator: Xianhong Hu, UNESCO

12. Rapporteur: Guilherme Canela De Souza Godoi, UNESCO

Detailed description of the workshop is available here http://igf2015.sched.org/event/4c1X/ws-128-mitigate-online-hate-speech-and-youth-radicalisation?iframe=no&w=&sidebar=yes&bg=no

Video link to the panel is available here https://www.youtube.com/watch?v=eIO1z4EjRG0

INTERMEDIARY LIABILITY

IX. Electronic Frontier Foundation, Centre for Internet Society India, Open Net Korea and Article 19 collaborated to organize a workshop on the Manila Principles on Intermediary Liability at Workshop Room 9 from 11:00 am to 12:00 pm on 13 November 2015. The workshop elaborated on the Manila Principles, a high level principle framework of best practices and safeguards for content restriction practices and addressing liability for intermediaries for third party content. The workshop saw particpants engaged in over lapping projects considering restriction practices coming togetehr to give feedback and highlight recent developments across liability regimes. Jyoti Panday laid down the key details of the Manila Principles framework in this session. The panelists for this workshop included the following.

Kelly Kim Open Net Korea,
Jyoti Panday, CIS India,
Gabrielle Guillemin, Article 19,
Rebecca McKinnon on behalf of UNESCO
Giancarlo Frosio, Center for Internet and Society, Stanford Law School
Nicolo Zingales, Tilburg University
Will Hudson, Google

Detailed description of the workshop is available here http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2423-2015-11-13-ws-242-the-manila-principles-on-intermediary-liability-workshop-room-9

Video link available here https://www.youtube.com/watch?v=kFLmzxXodjs

ACCESSIBILITY

X. Dynamic Coalition on Accessibility and Disability and Global Initiative for Inclusive ICTs organised a workshop on Empowering the Next Billion by Improving Accessibility at Workshop Room 6 from 9:00 am to 10:30 am on 13 November, 2015. The discussion focused on the need and ways to remove accessibility barriers which prevent over one billion potential users to benefit from the Internet, including for essential services. Sunil Abraham specifically spoke about the lack of compliance of existing ICT infrastructure with well established accessibility standards specifically relating to accessibility barriers in the disaster management process. He discussed the barriers faced by persons with physical or psychosocial disabilities. The panelists for this discussion were the following.

Francesca Cesa Bianchi, G3ICT
Cid Torquato, Government of Brazil
Carlos Lauria, Microsoft Brazil
Sunil Abraham, CIS India
Derrick L. Cogburn, Institute on Disability and Public Policy (IDPP) for the ASEAN(Association of Southeast Asian Nations) Region
Fernando H. F. Botelho, F123 Consulting
Gunela Astbrink, GSA InfoComm

Detailed description of the workshop is available here http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2438-2015-11-13-ws-253-empowering-the-next-billion-by-improving-accessibility-workshop-room-3

Video Link Empowering the next billion by improving accessibility https://www.youtube.com/watch?v=7RZlWvJAXxs

OPENNESS

XI. A workshop on FOSS & a Free, Open Internet: Synergies for Development was organized at Workshop Room 7 from 2:00 pm to 3:30 pm on 13 November, 2015. The discussion was focused on the increasing risk to openness of the internet and the ability of present & future generations to use technology to improve their lives. The panel shred different perspectives about the future co-development of FOSS and a free, open Internet; the threats that are emerging; and ways for communities to surmount these. Sunil Abraham emphasised the importance of free software, open standards, open access and access to knowledge and the lack of this mandate in the draft outcome document for upcoming WSIS+10 review and called for inclusion of the same. Pranesh Prakash further contributed to the discussion by emphasizing the need for free open source software with end‑to‑end encryption and traffic level encryption based on open standards which are decentralized and work through federated networks. The panellists for this discussion were the following.

Satish Babu, Technical Community, Chair, ISOC-TRV, Kerala, India
Judy Okite, Civil Society, FOSS Foundation for Africa
Mishi Choudhary, Private Sector, Software Freedom Law Centre, New York
Fernando Botelho, Private Sector, heads F123 Systems, Brazil
Sunil Abraham, CIS India
Pranesh Prakash, CIS India
Nnenna Nwakanma- WWW.Foundation
Yves MIEZAN EZO, Open Source strategy consultant
Corinto Meffe, Advisor to the President and Directors, SERPRO, Brazil
Frank Coelho de Alcantara, Professor, Universidade Positivo, Brazil
Caroline Burle, Institutional and International Relations, W3C Brazil Office and Center of Studies on Web Technologies

Detailed description of the workshop is available here http://www.intgovforum.org/cms/workshops/list-of-published-workshop-proposals

Transcript of the workshop is available here http://www.intgovforum.org/cms/187-igf-2015/transcripts-igf-2015/2468-2015-11-13-ws10-foss-and-a-free-open-internet-synergies-for-development-workshop-room-7

Video link available here https://www.youtube.com/watch?v=lwUq0LTLnDs

For more details visit https://cis-india.org/internet-governance/blog/summary-report-internet-governance-forum-2015

Predictive Policing: What is it, How it works, and its Legal Implications

Rohan George — 2015-11-24T16:31:41Z

This article reviews literature surrounding big data and predictive policing and provides an analysis of the legal implications of using predictive policing techniques in the Indian context.

Introduction

For the longest time, humans have been obsessed with prediction. Perhaps the most well-known oracle in history, Pythia, the infallible Oracle of Delphi was said to predict future events in hysterical outbursts on the seventh day of the month, inspired by the god Apollo himself. This fascination with informing ourselves about future events has hardly subsided in us humans. What has changed however is the methods we employ to do so. The development of Big data technologies for one, has seen radical applications into many parts of life as we know it, including enhancing our ability to make accurate predictions about the future.

One notable application of Big data into prediction caters to another basic need since the dawn of human civilisation, the need to protect our communities and cities. The word 'police' itself originates from the Greek word 'polis', which means city. The melding of these two concepts prediction and policing has come together in the practice of Predictive policing, which is the application of computer modelling to historical crime data and metadata to predict future criminal activity[1]. In the subsequent sections, I will attempt an introduction of predictive policing and explain some of the main methods within the domain of predictive policing. Because of the disruptive nature of these technologies, it will also be prudent to expand on the implications predictive technologies have for justice, privacy protections and protections against discrimination among others.

In introducing the concept of predictive policing, my first step is to give a short explanation about current predictive analytics techniques, because these techniques are the ones which are applied into a law enforcement context as predictive policing.

What is predictive analysis

Facilitated by the availability of big data, predictive analytics uses algorithms to recognise data patterns and predict future outcomes[2]. Predictive analytics encompasses data mining, predictive modeling, machine learning, and forecasting[3]. Predictive analytics also relies heavily on machine learning and artificial intelligence approaches [4]. The aim of such analysis is to identify relationships among variables that may not be immediately apparent using hypothesis-driven methods.[5] In the mainstream media, one of the most infamous stories about the use of predictive analysis comes from USA, regarding a department store Target and their data analytics practices [6]. Target mined data from purchasing patterns of people who signed onto their baby registry. From this they were able to predict approximately when customers may be due and target advertisements accordingly. In the noted story, they were so successful that they predicted pregnancy before the pregnant girl's father knew she was pregnant. [7]

Examples of predictive analytics

Predicting the success of a movie based on its online ratings[8]
Many universities, sometimes in partnership with other firms use predictive analytics to provide course recommendations to students, track student performance, personalize curriculum to individual students and foster networking between students.[9]
Predictive Analysis of Corporate Bond Indices Returns[10]

Relationship between predictive analytics and predictive policing

The same techniques used in many of the predictive methods mentioned above find application into some predictive policing methods. However two important points need to be raised:

First, predictive analytics is actually a subset of predictive policing. This is because while the steps in creating a predictive model, of defining a target variable, exposing your model to training data, selecting appropriate features and finally running predictive analysis [11] maybe the same in a policing context, there are other methods which may be used to predict crime, but which do not rely on data mining. These techniques may instead use other methods, such as some of those detailed below along with data about historical crime to generate predictions.

In her article "Policing by Numbers: Big Data and the Fourth Amendment"[12], Joh categorises 3 main applications of Big data into policing. These are Predictive Policing, Domain Awareness systems and Genetic Data Banks. Genetic data banks refer to maintaining large databases of DNA that was collected as part of the justice system. Issues arise when the DNA collected is repurposed in order to conduct familial searches, instead of being used for corroborating identity. Familial searches may have disproportionate impacts on minority races. Domain Awareness systems use various computer software and other digital surveillance tools such as Geographical Information Systems [13] or more illicit ones such as Black Rooms[14] to "help police create a software-enhanced picture of the present, using thousands of data points from multiple sources within a city" [15]. I believe Joh was very accurate in separating Predictive Policing from Domain Awareness systems, especially when it comes to analysing the implications of the various applications of Big data into policing.

In such an analysis of the implications of using predictive policing methods, the issues surrounding predictive technologies often get conflated with larger issues about the application of big data into law enforcement. That opens the debate up to questions about overly intrusive evidence gathering and mass surveillance systems, which though used along with predictive technology, are not themselves predictive in nature. In this article, I aim to concentrate on the specific implications that arise due to predictive methods.

One important point regarding the impact of predictive policing is how the insights that predictive policing methods offer are used. There is much support for the idea that predictive policing does not replace policing methods, but actually augments them. The RAND report specifically cites one myth about predictive policing as "the computer will do everything for you[16]". In reality police officers need to act on the recommendations provided by the technologies.

What is Predictive policing?

Predictive policing is the "application of analytical techniques-particularly quantitative techniques-to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions".[17] It is important to note that the use of data and statistics to inform policing is not new. Indeed, even twenty years ago, before the deluge of big data we have today, law enforcement regimes such as the New York Police Department (NYPD) were already using crime data in a major way. In order to keep track of crime trends, NYPD used the software CompStat[18] to map "crime statistics along with other indicators of problems, such as the locations of crime victims and gun arrests"[19]. The senior officers used the information provided by CompStat to monitor trends of crimes on a daily basis and such monitoring became an instrumental way to track the performance of police agencies[20]. CompStat has since seen application in many other jurisdictions [21].

But what is new is the amount of data available for collection, as well as the ease with which organisations can analyse and draw insightful results from that data. Specifically, new technologies allow for far more rigorous interrogation of data and wide-ranging applications, including adding greater accuracy to the prediction of future incidence of crime.

Predictive Policing methods

Some methods of predictive policing involve application of known standard statistical methods, while other methods involve modifying these standard techniques. Predictive techniques that forecast future criminal activities can be framed around six analytic categories. They all may overlap in the sense that multiple techniques are used to create actual predictive policing software and in fact it is similar theories of criminology which undergird many of these methods, but the categorisation in such a way helps clarify the concept of predictive policing. The basis for the categorisation below comes from a RAND Corporation report entitled 'Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations' [22], which is a comprehensive and detailed contribution to scholarship in this nascent area.

Hot spot analysis: Methods involving hot spot analysis attempt to "predict areas of increased crime risk based on historical crime data"[23]. The premise behind such methods lies in the adage that "crime tends to be lumpy" [24]. Hot Spot analysis seeks to map out these previous incidences of crime in order to inform potential future crime.

Regression methods: A regression aims to find relationships between independent variables (factors that may influence criminal activity) and certain variables that one aims to predict. Hence, this method would track more variables than just crime history.

Data mining techniques: Data mining attempts to recognise patterns in data and use it to make predictions about the future. One important variant in the various types of data mining methods used in policing are different types of algorithms that are used to mine data in different ways. These are dependent on the nature of the data the predictive model was trained on and will be used to interrogate in the future. Two broad categories of algorithms commonly used are clustering algorithms and classification algorithms:

· Clustering algorithms "form a class of data mining approaches that seek to group data into clusters with similar attributes" [25]. One example of clustering algorithms is spatial clustering algorithms, which use geospatial crime incident data to predict future hot spots for crime[26].

· Classification algorithms "seek to establish rules assigning a class or label to events"[27]. These algorithms use training data sets "to learn the patterns that determine the class of an observation"[28] The patterns identified by the algorithm will be applied to future data, and where applicable, the algorithm will recognise similar patterns in the data. This can be used to make predictions about future criminal activity for example.

Near-repeat methods: Near-repeat methods work off the assumption that future crimes will take place close to timing and location of current crimes. Hence, it could be postulated that areas of high crime will experience more crime in the near future[29]. This involves the use of a 'self-exciting' algorithm, very similar to algorithms modelling earthquake aftershocks [30]. The premise undergirding such methods is very similar to that of hot spot analysis.

Spatiotemporal analysis: Using "environmental and temporal features of the crime location" [31] as the basis for predicting future crime. By combining the spatiotemporal features of the crime area with crime incident data, police could use the resultant information to predict the location and time of future crimes. Examples of factors that may be considered include timing of crimes, weather, distance from highways, time from payday and many more.

Risk terrain analysis: Analyses other factors that are useful in predicting crimes. Examples of such factors include "the social, physical, and behavioural factors that make certain areas more likely to be affected by crime"[32]

Various methods listed above are used, often together, to predict the where and when a crime may take place or even potential victims. The unifying thread which relates these methods is their dependence on historical crime data.

Examples of predictive policing:

Most uses of predictive policing that have been studied and reviewed in scholarly work come from the USA, though I will detail one case study from Derbyshire, UK. Below is a collation of various methods that are a practical application of the methods raised above.

Hot Spot analysis in Sacramento: In February 2011, Sacramento Police Department began using hot spot analysis along with research on optimal patrol time to act as a sufficient deterrent to inform how they patrol high-risk areas. This policy was aimed at preventing serious crimes by patrolling these predicted hot spots. In places where there was such patrolling, serious crimes reduced by a quarter with no significant increases such crimes in surrounding areas[33].

Data Mining and Hot Spot Mapping in Derbyshire, UK: The Safer Derbyshire Partnership, a group of law enforcement agencies and municipal authorities sought to identify juvenile crime hotspots[34]. They used MapInfo software to combine "multiple discrete data sets to create detailed maps and visualisations of criminal activity, including temporal and spatial hotspots" [35]. This information informed law enforcement about how to optimally deploy their resources.

Regression models in Pittsburgh: Researchers used reports from Pittsburgh Bureau of Police about violent crimes and "leading indicator" [36] crimes, crimes that were relatively minor but which could be a sign of potential future violent offences. The researcher ran analysis of areas with violent crimes, which were used as the dependent variable in analysing whether violent crimes in certain areas could be predicted by the leading indicator data. From the 93 significant violent crime areas that were studied, 19 areas were successfully predicted by the leading indicator data.[37]

Risk terrain modelling analysis in Morris County, New Jersey: Police in Morris County, used risk terrain analysis to tackle violent crimes and burglaries. They considered five inputs in their model: "past burglaries, the address of individuals recently arrested for property crimes, proximity to major highways, the geographic concentration of young men and the location of apartment complexes and hotels." [38] The Morris County law enforcement officials linked the significant reductions in violent and property crime to their use of risk terrain modelling[39].

Near-repeat & hot spot analysis used by Santa Cruz Police Department: Uses PredPol software that applies the Mohler's algorithm [40] to a database with five years' worth of crime data to assess the likelihood of future crime occurring in the geographic areas within the city. Before going on shift, officers receive information identifying 15 such areas with the highest probability of crime[41]. The initiative has been cited as being very successful at reducing burglaries, and was used in Los Angeles and Richmond, Virginia[42].

Data Mining and Spatiotemporal analysis to predict future criminal activities in Chicago: Officers in Chicago Police Department made visits to people their software predicted were likely to be involved in violent crimes[43], guided by an algorithm-generated "Heat List"[44]. Some of the inputs used in the predictions include some types of arrest records, gun ownership, social networks[45] (police analysis of social networking is also a rising trend in predictive policing[46]) and generally type of people you are acquainted with [47] among others, but the full list of the factors are not public. The list sends police officers (or sometimes mails letters) to peoples' homes to offer social services or deliver warnings about the consequences for offending. Based in part on the information provided by the algorithm, officers may provide people on the Heat List information about vocational training programs or warnings about how Federal Law provides harsher punishments for reoffending[48].

Predictive policing in India

In this section, I map out some of the developments in the field of predictive policing within India. On the whole, predictive policing is still very new in India, with Jharkhand being the only state that appears to already have concrete plans in place to introduce predictive policing.

Jharkhand Police

The Jharkhand police began developing their IT infrastructure such as a Geographic Information System (GIS) and Server room when they received funding for Rs. 18.5 crore from the Ministry of Home Affairs[49]. The Open Group on E-governance (OGE), founded as a collaboration between the Jharkhand Police and National Informatics Centre[50], is now a multi-disciplinary group which takes on different projects related to IT[51]. With regards to predictive policing, some members of OGE began development in 2013 of data mining software which will scan online records that are digitised. The emerging crime trends "can be a building block in the predictive policing project that the state police want to try."[52]

The Jharkhand Police was also reported in 2012 to be in the final stages of forming a partnership with IIM-Ranchi[53]. It was alleged the Jharkhand police aimed to tap into IIM's advanced business analytics skills [54], skills that can be very useful in a predictive policing context. Mr Pradhan suggested that "predictive policing was based on intelligence-based patrol and rapid response"[55] and that it could go a long way to dealing with the threat of Naxalism in Jharkhand[56].

However, in Jharkhand, the emphasis appears to be targeted at developing a massive Domain Awareness system, collecting data and creating new ways to present that data to officers on the ground, instead of architecting and using predictive policing software. For example, the Jharkhand police now have in place "a Naxal Information System, Crime Criminal Information System (to be integrated with the CCTNS) and a GIS that supplies customised maps that are vital to operations against Maoist groups"[57]. The Jharkhand police's "Crime Analytics Dashboard" [58] shows the incidence of crime according to type, location and presents it in an accessible portal, providing up-to-date information and undoubtedly raises the situational awareness of the officers. Arguably, the domain awareness systems that are taking shape in Jharkhand would pave the way for predictive policing methods to be applied in the future. These systems and hot spot maps seem to be the start of a new age of policing in Jharkhand.

Predictive Policing Research

One promising idea for predictive policing in India comes from the research conducted by Lavanya Gupta and others entitled "Predicting Crime Rates for Predictive Policing"[59], which was a submission for the Gandhian Young Technological Innovation Award. The research uses regression modelling to predict future crime rates. Drawing from First Information Reports (FIRs) of violent crimes (murder, rape, kidnapping etc.) from Chandigarh Police, the team attempted "to extrapolate annual crime rate trends developed through time series models. This approach also involves correlating past crime trends with factors that will influence the future scope of crime, in particular demographic and macro-economic variables" [60]. The researchers used early crime data as the training data for their model, which after some testing, eventually turned out to have an accuracy of around 88.2%.[61] On the face of it, ideas like this could be the starting point for the introduction of predictive policing into India.

The rest of India's law enforcement bodies do not appear to be lagging behind. In the 44^th All India police science congress, held in Gandhinagar, Gujarat in March this year, one of the Themes for discussion was the "Role of Preventive Forensics and latest developments in Voice Identification, Tele-forensics and Cyber Forensics"[62].Mr A K Singh, (Additional Director General of Police, Administration) the chairman of the event also said in an interview that there was to be a round-table DGs (Director General of Police) held at the conference to discuss predictive policing[63]. Perhaps predictive policing in India may not be that far away from reality.

CCTNS and the building blocks of Predictive policing

The Ministry of Home Affairs conceived of a Crime and Criminals Tracking and Network System (CCTNS) as part of national e-Governance plans. According to the website of the National Crime Records Bureau (NCRB), CCTNS aims to develop "a nationwide networked infrastructure for evolution of IT-enabled state-of-the-art tracking system around 'investigation of crime and detection of criminals' in real time" [64]

The plans for predictive policing seem in the works, but first steps that are needed in India across police forces involve digitizing data collection by the police, as well as connecting law enforcement agencies. The NCRB's website described the current possibility of exchange of information between neighbouring police stations, districts or states as being "next to impossible"[65]. The aim of CCTNS is precisely to address this gap and integrate and connect the segregated law enforcement arms of the state in India, which would be a foundational step in any initiatives to apply predictive methods.

What are the implications of using predictive policing? Lessons from USA

Despite the moves by law enforcement agencies to adopt predictive policing, one reality is that the implications of predictive policing methods are far from clear. This section will examine these implications on the carriage of justice and its use in law, as well as how it impacts privacy concerns for the individual. It frames the existing debates surrounding these issues with predictive policing, and aims to apply these principles into an Indian context.

Justice, Privacy & IV Amendment

Two key concerns about how predictive policing methods may be used by law enforcement relate to how insights from predictive policing methods are acted upon and how courts interpret them. In the USA, this issue may finds its place under the scope of IV Amendment jurisprudence. The IV amendment states that all citizens are "secure from unreasonable searches and seizures of property by the government"[66]. In this sense, the IV amendment forms the basis for search and surveillance law in the USA.

A central aspect of the IV Amendment jurisprudence is drawn from United States v. Katz. In Katz, the FBI attached a microphone to the outside of a public phone booth to record the conversations of Charles Katz, who was making phone calls related to illegal gambling. The court ruled that such actions constituted a search within the auspices of the 4^th amendment. The ruling affirmed constitutional protection of all areas where someone has a "reasonable expectation of privacy"[67].

Later cases have provided useful tests for situations where government surveillance tactics may or may not be lawful, depending on whether it violates one's reasonable expectation of privacy. For example, in United States v. Knotts, the court held that "police use of an electronic beeper to follow a suspect surreptitiously did not constitute a Fourth Amendment search"[68]. In fact, some argue that that the Supreme Court's reasoning in such cases suggests " any 'scientific enhancement' of the senses used by the police to watch activity falls outside of the Fourth Amendment's protections if the activity takes place in public"[69]. This reasoning is based on the third party doctrine which holds that "if you voluntarily provide information to a third party, the IV Amendment does not preclude the government from accessing it without a warrant"[70]. The clearest exposition of this reasoning was in Smith v. Maryland, where the presiding judges noted that "this Court consistently has held that a person has no legitimate expectation of privacy in information he voluntarily turns over to third parties"[71].

However, the third party has seen some challenge in recent time. In United States v. Jones, it was ruled that the government's warrantless GPS tracking of his vehicle 24 hours a day for 28 days violated his Fourth Amendment rights[72]. Though the majority ruling was that warrantless GPS tracking constituted a search, it was in a concurring opinion written by Justice Sonya Sotomayor that such intrusive warrantless surveillance was said to infringe one's reasonable expectation of privacy. As Newell reflected on Sotomayor's opinion,

"Justice Sotomayor stated that the time had come for Fourth Amendment jurisprudence to discard the premise that legitimate expectations of privacy could only be found in situations of near or complete secrecy. Sotomayor argued that people should be able to maintain reasonable expectations of privacy in some information voluntarily disclosed to third parties"[73].

She said that the court's current reasoning on what constitutes reasonable expectations of privacy in information disclosed to third parties, such as email or phone records or even purchase histories, is "ill-suited to the digital age, in which people reveal a great deal of information about themselves to third parties in the course of carrying out mundane tasks"[74].

Predictive policing vs. Mass surveillance and Domain Awareness Systems

However, there is an important distinction to be drawn between these cases and evidence from predictive policing. This has to do with the difference in nature of the evidence collection. Arguably, from Jones and others, what we see is that use of mass surveillance and domain awareness systems, drawing from Joh's categorisation of domain awareness systems as being distinct from predictive policing mentioned above, could potentially encroach on one's reasonable expectation of privacy. However, I think that predictive policing, and the possible implications for justice associated with it, its predictive harms, are quite distinct from what has been heard by courts thus far.

The reason for distinct risks between predictive harms and privacy harms originating from information gathering is related to the nature of predictive policing technologies, and how they are used. It is highly unlikely that the evidence submitted by the State to indict an offender will be mainly predictive in nature. For example, would it be possible to convict an accused person solely on the premise that he was predicted to be highly likely to commit a crime, and that subsequently he did? The legal standard of proving guilt beyond a reasonable doubt [75] can hardly be met solely on predictive evidence for a multitude of reasons. Predictive policing methods could at most, be said to inform police about the risk of someone committing a crime or of crime happening at a certain location, as demonstrated above.

Predictive policing and Criminal Procedure

It may therefore pay to analyse how predictive policing may be used across the various processes within the criminal justice system. In fact, in an analysis of the various stages of criminal procedure, from opening an investigation to gathering evidence, followed by arrest, trial, conviction and sentencing, we see that as the individual gets subject to more serious incursions or sanctions by the state, it takes a higher standard of certainty about wrongdoing and a higher burden of proof, in order to legitimize that particular action.

Hence, at more advanced stages of the criminal justice process such as seeking arrest warrants or trial, it is very unlikely that predictive policing on its own can have a tangible impact, because the nature of predictive evidence is probability based. It aims to calculate the risk of future crime occurring based on statistical analysis of past crime data[76]. While extremely useful, probabilities on their own will not come remotely close meet the legal standards of proving 'guilt beyond reasonable doubt'. It may be at the earlier stages of the criminal justice process that evidence predictive policing might see more widespread application, in terms of applying for search warrants and searching suspicious people while on patrol.

In fact, in the law enforcement context, prediction as a concept is not new to justice. Both courts and law enforcement officials already make predictions about future likelihood of crimes. In the case of issuing warrants, the IV amendment makes provisions that law enforcement officials show that the potential search is based "upon probable cause"[77] in order for a judge to grant a warrant. In US v. Brinegar, probable cause was defined as existing "where the facts and circumstances within the officers' knowledge, and of which they have reasonably trustworthy information, are sufficient in themselves to warrant a belief by a man of reasonable caution that a crime is being committed" [78]. Again, this legal standard seems too high for predictive evidence meet.

However, the police also have an important role to play in preventing crimes by looking out for potential crimes while on patrol or while doing surveillance. When the police stop a civilian on the road to search him, reasonable suspicion must be established. This standard of reasonable suspicion was defined in most clearly in Terry v. Ohio, which required police to "be able to point to specific and articulable facts which, taken together with rational inferences from those facts, reasonably warrant that intrusion"[79]. Therefore, "reasonable suspicion that 'criminal activity may be afoot' is at base a prediction that the facts and circumstances warrant the reasonable prediction that a crime is occurring or will occur"[80]. Despite the assertion that "there are as of yet no reported cases on predictive policing in the Fourth Amendment context"[81], examining the impact of predictive policing on the doctrine of reasonable suspicion could be very instructive in understanding the implications for justice and privacy [82].

Predictive Policing and Reasonable Suspicion

Ferguson's insightful contribution to this area of scholarship involves the identification of existing areas where prediction already takes place in policing, and analogising them into a predictive policing context[83]. These three areas are: responding to tips, profiling, and high crime areas (hot spots).

Tips

Tips are pieces of information shared with the police by members of the public. Often tips, either anonymous or from known police informants, may predict future actions of certain people, and require the police to act on this information. The precedent for understanding the role of tips in probable cause comes from Illinois v. Gates[84]. It was held that "an informant's 'veracity,' 'reliability,' and 'basis of knowledge'-remain 'highly relevant in determining the value'"[85] of the said tip. Anonymous tips need to be detailed, timely and individualised enough[86] to justify reasonable suspicion [87]. And when the informant is known to be reliable, then his prior reliability may justify reasonable suspicion despite lacking a basis in knowledge[88].

Ferguson argues that whereas predictive policing cannot provide individualised tips, it is possible to consider reliable tips about certain areas as a parallel to predictive policing[89]. And since the courts had shown a preference for reliability even in the face of a weak basis in knowledge, it is possible to see the reasonable suspicion standard change in its application[90]. It also implies that IV protections may be different in places where crime is predicted to occur [91].

Profiling

Despite the negative connotations and controversial overtones at the mere sound of the word, profiling is already a method commonly used by law enforcement. For example, after a crime has been committed and general features of the suspect identified by witnesses, police often stop civilians who fit this description. Another example of profiling is common in combating drug trafficking[92], where agents keep track of travellers at airports to watch for suspicious behaviour. Based on their experience of common traits which distinguish drug traffickers from regular travellers (a profile), agents may search travellers if they fit the profile[93]. In the case of United States v. Sokolow[94], the courts "recognized that a drug courier profile is not an irrelevant or inappropriate consideration that, taken in the totality of circumstances, can be considered in a reasonable suspicion determination" [95]. Similar lines of thinking could be employed in observing people exchanging small amounts of money in an area known for high levels of drug activity, conceiving predictive actions as a form of profile[96].

It is valid to consider predictive policing as a form of profiling[97], but Ferguson argues that the predictive policing context means this 'new form' of profiling could change IV analysis. The premise behind such an argument lies in the fact that a prediction made by some algorithm about potential high risk of crime in a certain area, could be taken in conjunction observations of ordinarily innocuous events. Read in the totality of circumstances, these two threads may justify individual reasonable suspicion [98]. For example, a man looking into cars at a parking lot may not by itself justify reasonable suspicion, but taken together with a prediction of high risk of car theft at that locality, it may well justify reasonable suspicion. It is this impact of predictive policing, which influences the analysis of reasonable suspicion in a totality of circumstances that may represent new implications for courts looking at IV amendment protections.

Profiling, Predictive Policing and Discrimination

The above sections have already brought up the point that law enforcement agencies already utilize profiling methods in their operations. Also, as the sections on how predictive analytics works and on methods of predictive policing make clear, predictive policing definitely incorporates the development of profiles for predicting future criminal activity. Concerns about predictive models generate potentially discriminatory predictions therefore are very serious, and need addressing. Potential discrimination may be either overt, though far less likely, or unintended. A valuable case study of which sheds light on such discriminatory data mining practices can be found in US Labour law. It was shown how predictive models could be discriminatory at various stages, from conceptualising the model and training it with training data, to eventually selecting inappropriate features to search for [99]. It is also possible for data scientists to (intentionally or not) use proxies for identifiers like race, income level, health condition and religion. Barocas and Selbst argue that "the current distribution of relevant attributes-attributes that can and should be taken into consideration in apportioning opportunities fairly-are demonstrably correlated with sensitive attributes" [100]. Hence, what may result is unintended discrimination, as predictive models and their subjective and implicit biases are reflected in predicted decisions, or that the discrimination is not even accounted for in the first place. While I have not found any case law where courts have examined such situations in a criminal context, at the very least, law enforcement agencies need to be aware of these possibilities and guard against any forms of discriminatory profiling.

However, Ferguson argues that "the precision of the technology may in fact provide more protection for citizens in broadly defined high crime areas" [101]. This is because the label of a 'high-crime area' may no longer apply to large areas but instead to very specific areas of criminal activity. This implies that previously defined areas of high crime, like entire neighbourhoods may not be scrutinised in such detail. Instead, police now may be more precise in locating and policing areas of high crime, such as an individual street corner or a particular block of flats instead of an entire locality.

Hot Spots

Courts have also considered the existence of notoriously 'high-crime areas as part of considering reasonable suspicion[102]. This was seen in Illinois v. Wardlow [103], where the "high crime nature of an area can be considered in evaluating the officer's objective suspicion"[104]. Many cases have since applied this reasoning without scrutinising the predictive value of such a label. In fact, Ferguson asserts that such labelling has questionable evidential value[105]. He uses the facts of the Wardlow case itself to challenge the 'high crime area' factor. Ferguson cites the reasoning of one of the judges in the case:

"While the area in question-Chicago's District 11-was a low-income area known for violent crimes, how that information factored into a predictive judgment about a man holding a bag in the afternoon is not immediately clear."[106]

Especially because "the most basic models of predictive policing rely on past crimes"[107], it is likely that the predictive policing methods like hot spot or spatiotemporal analysis and risk terrain modelling may help to gather or build data models about high crime areas. Furthermore, the mathematical rigour of the predictive modelling could help clarify the term 'high crime area'. As Ferguson argues, "courts may no longer need to rely on the generalized high crime area terminology when more particularized and more relevant information is available" [108].

Summary

Ferguson synthesises four themes to which encapsulate reasonable suspicion analysis:

Predictive information is not enough on its own. Instead, it is "considered relevant to the totality of circumstances, but must be corroborated by direct police observation"[109].
The prediction must also "be particularized to a person, a profile, or a place, in a way that directly connects the suspected crime to the suspected person, profile, or place"[110].
It must also be detailed enough to distinguish a person or place from others not the focus of the prediction [111].
Finally, predicted information becomes less valuable over time. Hence it must be acted on quickly or be lost [112].

Conclusions from America

The main conclusion to draw from the analysis of the parallels between existing predictions in IV amendment law and predictive policing is that "predictive policing will impact the reasonable suspicion calculus by becoming a factor within the totality of circumstances test"[113]. Naturally, it reaffirms the imperative for predictive techniques to collect reliable data [114] and analyse it transparently[115]. Moreover, in order for courts to evaluate the reliability of the data and the processes used (since predictive methods become part of the reasonable suspicion calculus), courts need to be able to analyse the predictive process. This has implications for the how hearings may be conducted, for how legal adjudicators may require training and many more. Another important concern is that the model of predictive information and police corroboration or direct observation[116] may mean that in areas which were predicted to have low risk of crime, the reasonable suspicion doctrine works against law enforcement. There may be less effort paid to patrolling these other areas as a result of predictions.

Implications for India

While there have been no cases directly involving predictive policing methods, it would be prudent to examine the parts of Indian law which would inform the calculus on the lawfulness of using predictive policing methods. A useful lens to examine this might be found in the observation that prediction is not in itself a novel concept in justice, and is already used by courts and law enforcement in numerous circumstances.

Criminal Procedure in Non-Warrant Contexts

The most logical way to begin analysing the legal implications of predictive policing in India may probably involve identifying parallels between American and Indian criminal procedure, specifically searching for instances where 'reasonable suspicion' or some analogous requirement exists for justifying police searches.

In non-warrant scenarios, we find conditions for officers to conduct such a warrantless search in Section 165 of the Criminal Procedure Code (Cr PC). For clarity purposes I have stated section 165 (1) in full:

"Whenever an officer in charge of a police station or a police officer making an investigation has reasonable grounds for believing that anything necessary for the purposes of an investigation into any offence which he is authorised to investigate may be found in any place with the limits of the police station of which he is in charge, or to which he is attached, and that such thing cannot in his opinion be otherwise obtained without undue delay, such officer may, after recording in writing the grounds of his belief and specifying in such writing, so far as possible, the thing for which search is to be made, search, or cause search to be made, for such thing in any place within the limits of such station." [117]

However, India differs from the USA in that its Cr PC allows for police to arrest individuals without a warrant as well. As observed in Gulab Chand Upadhyaya vs State Of U.P, "Section 41 Cr PC gives the power to the police to arrest without warrant in cognizable offences, in cases enumerated in that Section. One such case is of receipt of a 'reasonable complaint' or 'credible information' or 'reasonable suspicion'" [118] Like above, I have stated section 41 (1) and subsection (a) in full:

"41. When police may arrest without warrant.

(1) Any police officer may without an order from a Magistrate and without a warrant, arrest any person-

(a) who has been concerned in any cognizable offence, or against whom a reasonable complaint has been made, or credible information has been received, or a reasonable suspicion exists, of his having been so concerned"[119]

In analysing the above sections of Indian criminal procedure from a predictive policing angle, one may find both similarities and differences between the proposed American approach and possible Indian approaches to interpreting or incorporating predictive policing evidence.

Similarity of 'reasonable suspicion' requirement

For one, the requirement for "reasonable grounds" or "reasonable suspicion" seems to be analogous to the American doctrine of reasonable suspicion. This suggests that the concepts used in forming reasonable suspicion, for the police to "be able to point to specific and articulable facts which, taken together with rational inferences from those facts, reasonably warrant that intrusion"[120] may also be useful in the Indian context.

One case which sheds light on an Indian interpretation of reasonable suspicion or grounds is State of Punjab v. Balbir Singh[121]. In that case, the court observes a requirement for "reason to believe that such an offence under Chapter IV has been committed and, therefore, an arrest or search was necessary as contemplated under these provisions"[122] in the context of Section 41 and 42 in The Narcotic Drugs and Psychotropic Substances Act, 1985[123]. In examining the requirement of having "reason to believe", the court draws on Partap Singh (Dr) v. Director of Enforcement, Foreign Exchange Regulation Act[124], where the judge observed that "the expression 'reason to believe' is not synonymous with subjective satisfaction of the officer. The belief must be held in good faith; it cannot be merely a pretence….."[125]

In light of this, the judge in Balbir Singh remarked that "whether there was such reason to believe and whether the officer empowered acted in a bona fide manner, depends upon the facts and circumstances of the case and will have a bearing in appreciation of the evidence" [126]. The standard considered by the court in Balbir Singh and Partap Singh is different from the 'reasonable suspicion' or 'reasonable grounds' standard as per Section 41 and 165 of Cr PC. But I think the discussion can help to inform our analysis of the idea of reasonableness in law enforcement actions. Of importance was the court requirement of something more than mere "pretence" as well as a belief held in good faith. This could suggest that in fact the reasoning in American jurisprudence about reasonable suspicion might be at least somewhat similar to how Indian courts view reasonable suspicion or grounds in the context of predictive policing, and therefore how we could similarly conjecture that predictive evidence could form part of the reasonable suspicion calculus in India as well.

Difference in judicial treatment of illegally obtained evidence - Indian lack of exclusionary rules

However, the apparent similarity of how police in America and India may act in non-warrant situations - guided by the idea of reasonable suspicion - is only veneered by linguistic parallels. Despite the existence of such conditions which govern the searches without a warrant, I believe that Indian courts currently may provide far less protection against unlawful use of predictive technologies. The main premise behind this argument is that Indian courts refuse to exclude evidence that was obtained in breaches of the conditions of sections of the Cr PC. What exists in place of evidentiary safeguards is a line of cases in which courts routinely admit unlawfully or illegally obtained evidence. Without protections against unlawfully gathered evidence being considered relevant by courts, any regulations on search or conditions to be met before a search is lawful become ineffective. Evidence may simply enter the courtroom through a backdoor.

In the USA, this is by and large, not the case. Although there are exceptions to these rules, exclusionary rules are set out to prevent admission of evidence which violates the constitution[127]. "The exclusionary rule applies to evidence gained from an unreasonable search or seizure in violation of the Fourth Amendment "[128]. Mapp v. Ohio [129] set the precedent for excluding unconstitutionally gathered evidence, where the court ruled that "all evidence obtained by searches and seizures in violation of the Federal Constitution is inadmissible in a criminal trial in a state court" [130].

Any such evidence which then leads law enforcement to collect new information may also be excluded, as part of the "fruit of the poisonous tree" doctrine[131], established in Silverthorne Lumber Co. v. United States [132]. The doctrine is a metaphor which suggests that if the source of certain evidence is tainted, so is 'fruit' or derivatives from that unconstitutional evidence. One such application was in Beck v. Ohio[133], where the courts overturned a petitioner's conviction because the evidence used to convict him was obtained via an unlawful arrest.

However in India's context, there is very little protection against the admission and use of unlawfully gathered evidence. In fact, there are a line of cases which lay out the extent of consideration given to unlawfully gathered evidence - both cases that specifically deal with the rules as per the Indian Cr PC as well as cases from other contexts - which follow and develop this line of reasoning of allowing illegally obtained evidence.

One case to pay attention to is State of Maharastra v. Natwarlal Damodardas Soni - in this case, the Anti-Corruption Bureau searched the house of the accused after receiving certain information as a tip. The police "had powers under the Code of Criminal Procedure to search and seize this gold if they had reason to believe that a cognizable offence had been committed in respect thereof"[134]. Justice Sarkaria, in delivering his judgement, observed that for argument's sake, even if the search was illegal, "then also, it will not affect the validity of the seizure and further investigation"[135]. The judge drew reasoning from Radhakishan v. State of U.P[136]. This which was a case involving a postman who had certain postal items that were undelivered recovered from his house. As the judge in Radhakishan noted:

"So far as the alleged illegality of the search is concerned, it is sufficient to say that even assuming that the search was illegal the seizure of the articles is not vitiated. It may be that where the provisions of Sections 103 and 165 of the Code of Criminal Procedure, are contravened the search could be resisted by the person whose premises are sought to be searched. It may also be that because of the illegality of the search the court may be inclined to examine carefully the evidence regarding the seizure. But beyond these two consequences no further consequence ensues." [137]

Shyam Lal Sharma v. State of M.P.[138] was also drawn upon, where it was held that "even if the search is illegal being in contravention with the requirements of Section 165 of the Criminal Procedure Code, 1898, that provision ceases to have any application to the subsequent steps in the investigation"[139].

Even in Gulab Chand Upadhyay, mentioned above, the presiding judge contended that even "if arrest is made, it does not require any, much less strong, reasons to be recorded or reported by the police. Thus so long as the information or suspicion of cognizable offence is "reasonable" or "credible", the police officer is not accountable for the discretion of arresting or no arresting"[140].

A more complete articulation of the receptiveness of Indian courts to admit illegally gathered evidence can be seen in the aforementioned Balbir Singh. The judgement aimed to:

"dispose of one of the contentions that failure to comply with the provisions of Cr PC in respect of search and seizure even up to that stage would also vitiate the trial. This aspect has been considered in a number of cases and it has been held that the violation of the provisions particularly that of Sections 100, 102, 103 or 165 Cr PC strictly per se does not vitiate the prosecution case. If there is such violation, what the courts have to see is whether any prejudice was caused to the accused and in appreciating the evidence and other relevant factors, the courts should bear in mind that there was such a violation and from that point of view evaluate the evidence on record."[141]

The judges then consulted a series of authorities on the failure to comply with provisions of the Cr PC:

State of Punjab v. Wassan Singh[142]: "irregularity in a search cannot vitiate the seizure of the articles"[143].
Sunder Singh v. State of U.P[144]: 'irregularity cannot vitiate the trial unless the accused has been prejudiced by the defect and it is also held that if reliable local witnesses are not available the search would not be vitiated."[145]
Matajog Dobey v.H.C. Bhari[146]: "when the salutory provisions have not been complied with, it may, however, affect the weight of the evidence in support of the search or may furnish a reason for disbelieving the evidence produced by the prosecution unless the prosecution properly explains such circumstance which made it impossible for it to comply with these provisions."[147]
R v. Sang[148]: "reiterated the same principle that if evidence was admissible it matters not how it was obtained."[149] Lord Diplock, one of the Lords adjudicating the case, observed that "however much the judge may dislike the way in which a particular piece of evidence was obtained before proceedings were commenced, if it is admissible evidence probative of the accused's guilt "it is no part of his judicial function to exclude it for this reason". [150] As the judge in Balbir Singh quoted from Lord Diplock, a judge "has no discretion to refuse to admit relevant admissible evidence on the ground that it was obtained by improper or unfair means. The court is not concerned with how it was obtained."[151]

The vast body of case law presented above provides observers with a clear image of the courts willingness to admit and consider illegally obtained evidence. The lack of safeguards against admission of unlawful evidence are important from the standpoint of preventing the excessive or unlawful use of predictive policing methods. The affronts to justice and privacy, as well as the risks of profiling, seem to become magnified when law enforcement use predictive methods more than just to augment their policing techniques but to replace some of them. The efficacy and expediency offered by using predictive policing needs to be balanced against the competing interest of ensuring rule of law and due process. In the Indian context, it seems courts sparsely consider this competing interest.

Naturally, weighing in on which approach is better depends on a multitude of criteria like context, practicality, societal norms and many more. It also draws on existing debates in administrative law about the role of courts, which may emphasise protecting individuals and preventing excessive state power (red light theory) or emphasise efficiency in the governing process with courts assisting the state to achieve policy objectives (green light theory) [152].

A practical response may be that India should aim to embrace both elements and balance them appropriately, although what an appropriate balance again may vary. There are some who claim that this balance already exists in India. Evidence for such a claim may come from R.M. Malkani v. State of Maharashtra[153], where the court considered whether an illegally tape-recorded conversation could be admissible. In its reasoning, the court drew from Kuruma, Son of Kanju v. R. [154], noting that

" if evidence was admissible it matters not how it was obtained. There is of course always a word of caution. It is that the Judge has a discretion to disallow evidence in a criminal case if the strict rules of admissibility would operate unfairly against the accused. That caution is the golden rule in criminal jurisprudence"[155].

While this discretion exists at least principally in India, in practice the cases presented above show that judges rarely exercise that discretion to prevent or bar the admission of illegally obtained evidence or evidence that was obtained in a manner that infringed the provisions governing search or arrest in the Cr PC. Indeed, the concern is that perhaps the necessary safeguards required to keep law enforcement practices, including predictive policing techniques, in check would be better served by a greater focus on reconsidering the legality of unlawfully gathered evidence. If not, evidence which should otherwise be inadmissible may find its way into consideration by existing legal backdoors.

Risk of discriminatory predictive analysis

Regarding the risk of discriminatory profiling, Article 15 of India's Constitution[156] states that "the State shall not discriminate against any citizen on grounds only of religion, race, caste, sex, place of birth or any of them" [157]. The existence of constitutional protection for such forms of discrimination suggests that India will be able to guard against discriminatory predictive policing. However, as mentioned before, predictive analytics often discriminates institutionally, "whereby unconscious implicit biases and inertia within society's institutions account for a large part of the disparate effects observed, rather than intentional choices"[158]. As in most jurisdictions, preventing these forms of discrimination are much harder. Especially in a jurisdiction whose courts are already receptive to allowing admission of illegally obtained evidence, the risk of discriminatory data mining or prejudiced algorithms being used by police becomes magnified. Because the discrimination may be unintentional, it may be even harder for evidence from discriminatory predictive methods to be scrutinised or when applicable, dismissed by the courts.

Conclusion for India

One thing which is eminently clear from the analysis of possible interpretations of predictive evidence is that Indian Courts have had no experience with any predictive policing cases, because the technology itself is still at a nascent stage. There is in fact a long way to go before predictive policing will become used on a scale similar to that of USA for example.

But, even in places where predictive policing is used much more prominently, there is no precedent to observe how courts may view predictive policing. Ferguson's method of locating analogous situations to predictive policing which courts have already considered is one notable approach, but even this does not provide complete answer. One of his main conclusions that predictive policing will affect the reasonable suspicion calculus, or in India's case, contribute to 'reasonable grounds' in some ways, is perhaps the most valid one.

However, what provides more cause for concern in India's context are the limited protections against use of unlawfully gathered evidence. The lack of 'exclusionary rules' unlike those present in the US amplifies the various risks of predictive policing because individuals have little means of redress in such situations where predictive policing may be used unjustly against them.

Yet, the promise of predictive policing remains undeniably attractive for India. The successes predictive policing methods seem to have had In the US and UK coupled with the more efficient allocation of law enforcement's resources as a consequence of adapting predictive policing evidence this point. The government recognises this and seems to be laying the foundation and basic digital infrastructure required to utilize predictive policing optimally. One ought also to ask whether it is the even within the court's purview to decide what kind of policing methods are to be permissible through evaluating the nature of evidence. There is a case to be made for the legislative arm of the state to provide direction on how predictive policing is to be used in India. Perhaps the law must also evolve with the changes in technology, especially if courts are to scrutinise the predictive policing methods themselves.

[1] Joh, Elizabeth E. "Policing by Numbers: Big Data and the Fourth Amendment." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 1, 2014. http://papers.ssrn.com/abstract=2403028.

[2] Tene, Omer, and Jules Polonetsky. "Big Data for All: Privacy and User Control in the Age of Analytics." Northwestern Journal of Technology and Intellectual Property 11, no. 5 (April 17, 2013): 239.

[3] Datta, Rajbir Singh. "Predictive Analytics: The Use and Constitutionality of Technology in Combating Homegrown Terrorist Threats." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 1, 2013. http://papers.ssrn.com/abstract=2320160.

[4] Johnson, Jeffrey Alan. "Ethics of Data Mining and Predictive Analytics in Higher Education." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 8, 2013. http://papers.ssrn.com/abstract=2156058.

[5] Ibid.

[6] Duhigg, Charles. "How Companies Learn Your Secrets." The New York Times, February 16, 2012. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.

[7] Ibid.

[8] Lijaya, A, M Pranav, P B Sarath Babu, and V R Nithin. "Predicting Movie Success Based on IMDB Data." International Journal of Data Mining Techniques and Applications 3 (June 2014): 365-68.

[9] Johnson, Jeffrey Alan. "Ethics of Data Mining and Predictive Analytics in Higher Education." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 8, 2013. http://papers.ssrn.com/abstract=2156058.

[10] Sangvinatsos, Antonios A. "Explanatory and Predictive Analysis of Corporate Bond Indices Returns." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, June 1, 2005. http://papers.ssrn.com/abstract=891641.

[11] Barocas, Solon, and Andrew D. Selbst. "Big Data's Disparate Impact." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 13, 2015. http://papers.ssrn.com/abstract=2477899.

[12] Joh, supra note 1.

[13] US Environmental Protection Agency. "How We Use Data in the Mid-Atlantic Region." US EPA. Accessed November 6, 2015. http://archive.epa.gov/reg3esd1/data/web/html/.

[14] See here for details of blackroom.

[15] Joh, supra note 1, at pg 48.

[16] Perry, Walter L., Brian McInnis, Carter C. Price, Susan Smith and John S. Hollywood. Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica, CA: RAND Corporation, 2013. http://www.rand.org/pubs/research_reports/RR233. Also available in print form.

[17] Ibid, at pg 2.

[18] Chan, Sewell. "Why Did Crime Fall in New York City?" City Room. Accessed November 6, 2015. http://cityroom.blogs.nytimes.com/2007/08/13/why-did-crime-fall-in-new-york-city/.

[19] Bureau of Justice Assistance. "COMPSTAT: ITS ORIGINS, EVOLUTION, AND FUTURE IN LAW ENFORCEMENT AGENCIES," 2013. http://www.policeforum.org/assets/docs/Free_Online_Documents/Compstat/compstat%20-%20its%20origins%20evolution%20and%20future%20in%20law%20enforcement%20agencies%202013.pdf.

[20] 1996 internal NYPD article "Managing for Results: Building a Police Organization that Dramatically Reduces Crime, Disorder, and Fear."

[21] Bratton, William. "Crime by the Numbers." The New York Times, February 17, 2010. http://www.nytimes.com/2010/02/17/opinion/17bratton.html.

[22] RAND CORP, supra note 16.

[23] RAND CORP, supra note 16, at pg 19.

[24] Joh, supra note 1, at pg 44.

[25] RAND CORP, supra note 16, pg 38.

[26] Ibid.

[27] RAND CORP, supra note 16, at pg 39.

[28] Ibid.

[29] RAND CORP, supra note 16, at pg 41.

[30] Data-Smart City Solutions. "Dr. George Mohler: Mathematician and Crime Fighter." Data-Smart City Solutions, May 8, 2013. http://datasmart.ash.harvard.edu/news/article/dr.-george-mohler-mathematician-and-crime-fighter-166.

[31] RAND CORP, supra note 16, at pg 44.

[32] Joh, supra note 1, at pg 45.

[33] Ouellette, Danielle. "Dispatch - A Hot Spots Experiment: Sacramento Police Department," June 2012. http://cops.usdoj.gov/html/dispatch/06-2012/hot-spots-and-sacramento-pd.asp.

[34] Pitney Bowes Business Insight. "The Safer Derbyshire Partnership." Derbyshire, 2013. http://www.mapinfo.com/wp-content/uploads/2013/05/safer-derbyshire-casestudy.pdf.

[35] Ibid.

[36] Daniel B Neill, Wilpen L. Gorr. "Detecting and Preventing Emerging Epidemics of Crime," 2007.

[37] RAND CORP, supra note 16, at pg 33.

[38] Joh, supra note 1, at pg 46.

[39] Paul, Jeffery S, and Thomas M. Joiner. "Integration of Centralized Intelligence with Geographic Information Systems: A Countywide Initiative." Geography and Public Safety 3, no. 1 (October 2011): 5-7.

[40] Mohler, supra note 30.

[41] Ibid.

[42] Moses, B., Lyria, & Chan, J. (2014). Using Big Data for Legal and Law Enforcement
Decisions: Testing the New Tools (SSRN Scholarly Paper No. ID 2513564). Rochester, NY: Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2513564

[43] Gorner, Jeremy. "Chicago Police Use Heat List as Strategy to Prevent Violence." Chicago Tribune. August 21, 2013. http://articles.chicagotribune.com/2013-08-21/news/ct-met-heat-list-20130821_1_chicago-police-commander-andrew-papachristos-heat-list.

[44] Stroud, Matt. "The Minority Report: Chicago's New Police Computer Predicts Crimes, but Is It Racist?" The Verge. Accessed November 13, 2015. http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist.

[45] Moser, Whet. "The Small Social Networks at the Heart of Chicago Violence." Chicago Magazine, December 9, 2013. http://www.chicagomag.com/city-life/December-2013/The-Small-Social-Networks-at-the-Heart-of-Chicago-Violence/.

[46] Lester, Aaron. "Police Clicking into Crimes Using New Software." Boston Globe, March 18, 2013. https://www.bostonglobe.com/business/2013/03/17/police-intelligence-one-click-away/DzzDbrwdiNkjNMA1159ybM/story.html.

[47] Stanley, Jay. "Chicago Police 'Heat List' Renews Old Fears About Government Flagging and Tagging." American Civil Liberties Union, February 25, 2014. https://www.aclu.org/blog/chicago-police-heat-list-renews-old-fears-about-government-flagging-and-tagging.

[48] Rieke, Aaron, David Robinson, and Harlan Yu. "Civil Rights, Big Data, and Our Algorithmic Future," September 2014. https://bigdata.fairness.io/wp-content/uploads/2015/04/2015-04-20-Civil-Rights-Big-Data-and-Our-Algorithmic-Future-v1.2.pdf.

[49] Edmond, Deepu Sebastian. "Jhakhand's Digital Leap." Indian Express, September 15, 2013. http://www.jhpolice.gov.in/news/jhakhands-digital-leap-indian-express-15092013-18219-1379316969.

[50] Jharkhand Police. "Jharkhand Police IT Vision 2020 - Effective Shared Open E-Governance." 2012. http://jhpolice.gov.in/vision2020. See slide 2

[51] Edmond, supra note 49.

[52] Edmond, supra note 49.

[53] Kumar, Raj. "Enter, the Future of Policing - Cops to Team up with IIM Analysts to Predict & Prevent Incidents." The Telegraph. August 28, 2012. http://www.telegraphindia.com/1120828/jsp/jharkhand/story_15905662.jsp#.VkXwxvnhDWK.

[54] Ibid.

[55] Ibid.

[56] Ibid.

[57] See supra note 49.

[58] See here for Jharkhand Police crime dashboard.

[59] Lavanya Gupta, and Selva Priya. "Predicting Crime Rates for Predictive Policing." Gandhian Young Technological Innovation Award, December 29, 2014. http://gyti.techpedia.in/project-detail/predicting-crime-rates-for-predictive-policing/3545.

[60] Gupta, Lavanya. "Minority Report: Minority Report." Accessed November 13, 2015. http://cmuws2014.blogspot.in/2015/01/minority-report.html.

[61] See supra note 59.

[62] See here for details about 44th All India Police Science Congress.

[63] India, Press Trust of. "Police Science Congress in Gujarat to Have DRDO Exhibition." Business Standard India, March 10, 2015. http://www.business-standard.com/article/pti-stories/police-science-congress-in-gujarat-to-have-drdo-exhibition-115031001310_1.html.

[64] National Crime Records Bureau. "About Crime and Criminal Tracking Network & Systems - CCTNS." Accessed November 13, 2015. http://ncrb.gov.in/cctns.htm.

[65] Ibid. (See index page)

[66] U.S. Const. amend. IV, available here

[67] United States v Katz, 389 U.S. 347 (1967) , see here

[68] See supra note 1, at pg 60.

[69] See supra note 1, at pg 60.

[70] Villasenor, John. "What You Need to Know about the Third-Party Doctrine." The Atlantic, December 30, 2013. http://www.theatlantic.com/technology/archive/2013/12/what-you-need-to-know-about-the-third-party-doctrine/282721/.

[71] Smith v Maryland, 442 U.S. 735 (1979), see here

[72] United States v Jones, 565 U.S. ___ (2012), see here

[73] Newell, Bryce Clayton. "Local Law Enforcement Jumps on the Big Data Bandwagon: Automated License Plate Recognition Systems, Information Privacy, and Access to Government Information." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, October 16, 2013. http://papers.ssrn.com/abstract=2341182, at pg 24.

[74] See supra note 72.

[75] Dahyabhai Chhaganbhai Thakker vs State Of Gujarat, 1964 AIR 1563

[76] See supra note 16.

[77] See supra note 66.

[78] Brinegar v. United States, 338 U.S. 160 (1949), see here

[79] Terry v. Ohio, 392 U.S. 1 (1968), see here

[80] Ferguson, Andrew Guthrie. "Big Data and Predictive Reasonable Suspicion." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, April 4, 2014. http://papers.ssrn.com/abstract=2394683, at pg 287. See also supra note 79.

[81] See supra note 80.

[82] See supra note 80.

[83] See supra note 80.

[84] See supra note 80, at pg 289.

[85] Illinois v. Gates, 462 U.S. 213 (1983). See here

[86] See Alabama v. White, 496 U.S. 325 (1990). See here

[87] See supra note 80, at pg 291.

[88] See supra note 80, at pg 293.

[89] See supra note 80, at pg 308.

[90] Ibid.

[91] Ibid.

[92] Larissa Cespedes-Yaffar, Shayona Dhanak, and Amy Stephenson. "U.S. v. Mendenhall, U.S. v. Sokolow, and the Drug Courier Profile Evidence Controversy." Accessed July 6, 2015. http://courses2.cit.cornell.edu/sociallaw/student_projects/drugcourier.html.

[93] Ibid.

[94] United States v. Sokolow, 490 U.S. 1 (1989), see here

[95] See supra note 80, at pg 295.

[96] See supra note 80, at pg 297.

[97] See supra note 80, at pg 308.

[98] See supra note 80, at pg 310.

[99] See supra note 11.

[100] See supra note 11.

^{^[101]} See supra note 80, at pg 303.

[102] See supra note 80, at pg 300.

[103] Illinois v. Wardlow, 528 U.S. 119 (2000), see here

[104] Ibid.

[105] See supra note 80, at pg 301.

[106] Ibid.

[107] See supra note 1, at pg 42.

[108] See supra note 80, at pg 303.

[109] See supra note 80, at pg 303.

[110] Ibid.

[111] Ibid.

[112] Ibid.

[113] See supra note 80, at pg 312.

[114] See supra note 80, at pg 317.

[115] See supra note 80, at pg 319.

[116] See supra note 80, at pg 321.

[117] Section 165 Indian Criminal Procedure Code, see here

[118] Gulab Chand Upadhyaya vs State Of U.P, 2002 CriLJ 2907

[119] Section 41 Indian Criminal Procedure Code

[120] See supra note 79

[121] State of Punjab v. Balbir Singh. (1994) 3 SCC 299

[122] Ibid.

[123] Section 41 and 42 in The Narcotic Drugs and Psychotropic Substances Act 1985, see here

[124] Partap Singh (Dr) v. Director of Enforcement, Foreign Exchange Regulation Act. (1985) 3 SCC 72 : 1985 SCC (Cri) 312 : 1985 SCC (Tax) 352 : AIR 1985 SC 989

[125] Ibid, at SCC pg 77-78.

[126] See supra note 121, at pg 313.

[127] Carlson, Mr David. "Exclusionary Rule." LII / Legal Information Institute, June 10, 2009. https://www.law.cornell.edu/wex/exclusionary_rule.

[128] Ibid.

[129] Mapp v Ohio, 367 U.S. 643 (1961), see here

[130] Ibid.

[131] Busby, John C. "Fruit of the Poisonous Tree." LII / Legal Information Institute, September 21, 2009. https://www.law.cornell.edu/wex/fruit_of_the_poisonous_tree.

[132] Silverthorne Lumber Co., Inc. v. United States, 251 U.S. 385 (1920), see here.

[133] Beck v. Ohio, 379 U.S. 89 (1964), see here.

[134] State of Maharashtra v. Natwarlal Damodardas Soni, (1980) 4 SCC 669, at 673.

[135] Ibid.

[136] Radhakishan v. State of U.P. [AIR 1963 SC 822 : 1963 Supp 1 SCR 408, 411, 412 : (1963) 1 Cri LJ 809]

[137] Ibid, at SCR pg 411-12.

[138] Shyam Lal Sharma v. State of M.P. (1972) 1 SCC 764 : 1974 SCC (Cri) 470 : AIR 1972 SC 886

[139] See supra note 135, at page 674.

[140] See supra note 119, at para. 10.

[141] See supra note 121, at pg 309.

[142] State of Punjab v. Wassan Singh, (1981) 2 SCC 1 : 1981 SCC (Cri) 292

[143] See supra note 121, at pg 309.

[144] Sunder Singh v. State of U.P, AIR 1956 SC 411 : 1956 Cri LJ 801

[145] See supra note 121, at pg 309.

[146] Matajog Dobey v.H.C. Bhari, AIR 1956 SC 44 : (1955) 2 SCR 925 : 1956 Cri LJ 140

[147] See supra note 121, at pg 309.

[148] R v. Sang, (1979) 2 All ER 1222, 1230-31

[149] See supra note 121, at pg 309.

[150] Ibid.

[151] Ibid.

[152] Harlow, Carol, and Richard Rawlings. Law and Administration. 3rd ed. Law in Context. Cambridge University Press, 2009.

[153] R.M. Malkani v. State of Maharashtra, (1973) 1 SCC 471

[154] Kuruma, Son of Kanju v. R., (1955) AC 197

[155] See supra note 154, at 477.

[156] Indian Const. Art 15, see here

[157] Ibid.

[158] See supra note 11.

For more details visit https://cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications