Centre for Internet and Society

Should Nandan Nilekani's Aadhaar project, for identity proof and welfare delivery, exist at all?

praskrishna — 2014-04-14T10:27:57Z

The foundation of Aadhaar—a Congress flagship project to give every Indian a unique identity number and then use it to deliver services—has been under assault in the past three months.

The article by M. Rajshekhar was published in the Economic Times on April 3, 2014. Sunil Abraham is quoted.

Political, legal, reputational.

The political backlash is coming from leaders of BJP, the Congress' principal rival. Meenakshi Lekhi and Ananth Kumar are not, by any stretch of the imagination, the first or the last word on policy matters in the BJP, but they mince no words when they say that if their party forms a government, it will trash Aadhaar —a project that has delivered a unique ID to half of India and on which Rs 3,800 crore has been spent.

Even as BJP's loose cannons fired, the Supreme Court repeated on March 24 that the government cannot make Aadhaar mandatory to access welfare services like pensions and LPG subsidy. The same day, investigative journalism portal Cobrapost aired videos that allegedly showed agencies agreeing to enrol people from neighbouring countries for a bribe.

The BJP piled on. "It (Aadhaar) has served no purpose. They have issued cards to illegal migrants. We want citizenship cards," says Prakash Javadekar, spokesperson of BJP. His party does not have an official policy line on Aadhaar as yet, but another of its leaders, Yashwant Sinha, headed the Parliamentary panel that, in 2011, severely criticised and rejected the draft bill that provided the legal framework for Aadhaar. "We are for direct benefit transfer but not on the basis of Aadhaar, which is a very badly-designed scheme," Sinha told CNBC-TV18 on January 31. "We will give it to all citizens of India on the basis of NPR."

On the campaign trail in Bangalore, Nandan Nilekani, the chief architect and implementer of Aadhaar, defends his work as the chairman of Unique Identification Authority of India (UIDAI). "Aadhaar is a pro-development and an anti-corruption platform," says Nilekani, who was brought in by the Congress high command in 2009 and is contesting these elections on a party ticket against BJP's Kumar in Bangalore South. "It is a pity that some vested interests with narrow political and other motives are trying to stall the project."

Lost in those binaries are the objectives of Aadhaar, to universalise identity proof and to use it to plug leakages in delivery of welfare services. UIDAI, led by a hands-on Nilekani, pursued this agenda with a certain authority, great speed and an overriding emphasis on technology, all of which delivered outcomes.

But they also contributed to shortcomings that saw the project stumble on its way and for which it is now being critiqued. "This is the only way transformation takes place," says K Koshy, who was part of the team that conceptualised Aadhaar and is now with Ernst & Young. "When you know the ultimate system is workable, you sort out the problems as you go along."

Except, given the political winds blowing, it's anyone's guess what the new dispensation will feel about Aadhaar and UIDAI, from where Nilekani resigned on March 13 and which is seeing many officers who came from other parts of the government, on deputation, returning. Will the new dispensation see Aadhaar as an idea that is sound but with parts that need strengthening? Or, will they see it as an idea that is, by itself, fallacious? "I don't know where this is going," says Abhijit Sen, member, Planning Commission, under which UIDAI is housed.

At one level, it's a political question. "The next Parliament will have to decide what UIDAI can and cannot do," says Sen. At another level, even that political answer will stem from the answers to three questions that go to the core of what Aadhaar was meant to be and where it fell short.

1. Does Aadhaar Provide a Unique and Definitive Identity?

Yes and no. UIDAI collects two sets of information from an individual. The first is biometrics: prints of all 10 fingers and a scan of the iris in both eyes. Biometric data, which is supposed to be unique to every individual, is used to assign a unique number to the individual. The second set is basic personal information: name, address, father's name, date of birth and address. Individuals can show existing documents—like voter's I-card or passport —as verification. For those who did not have identification documents, UIDAI allowed certain people to attest for them.

Aadhaar is better at identifying individuals through their biometrics than ensuring the accuracy of their add-on data. This is partly due to its design. When Aadhaar was being conceptualised, says Shrikant Nadhamuni, who headed technology for UIDAI: "We wanted to move the ID game—from a state where some people had no ID and others had paper ID to something beyond even what Singapore had, in the form of smart cards, to online. Like biometric. Which is the future.

Here, your presence is enough to vet your ID." This is also partly due to how UIDAI did its enrolments. Shortly after taking charge, Nilekani announced UIDAI would issue 600 million Aadhaar numbers by March 2014. The initial plan was that the National Population Register (NPR), which conducts the decadal Census and which is housed under the ministry of home, would do the enrolments— capturing biometrics and information— and UIDAI would only issue the numbers.

Soon after, Nilekani decided he could not meet his 600 million target if he waited for NPR to give him biometric packets, and offered to do enrolments too. To meet the target, UIDAI wanted to outsource enrolment to multiple vendors. And compared to NPR, UIDAI collected very little demographic data. UIDAI appointed public and private companies as enrolment agencies. Quality issues arose. "90% of the larger enrolment agencies offloaded the work to local, small-time guys," says the head of a Gurgaon-based enrolment agency, not wanting to be named.

Instances of incomplete addresses, spelling mistakes, people bribing enrolment staff to obtain numbers, emerged. "There is always a trade off between inclusion and accuracy," says Nilekani. "And the fact that these errors happened only shows that the gates were kept wide enough to ensure there would be no exclusion." "The Aadhaar database is based on very weak data," says Sunil Abraham, the head of Bangalore-based Centre for Internet and Society, an Internet and governance think-tank. "It is basically linking biometrics to a person and the name/address he claims as his." This weakness started showing up as the government began to deliver welfare services by transferring money directly into bank accounts of beneficiaries, using Aadhaar. The first step was to add the Aadhaar number to the department and bank databases.

Reddy Subramanyam, joint secretary of NREGA, tried to seed Aadhaar numbers into his database of NREGA workers. "The current matching is just 25-30%." The mismatch arises because, say, the name will be S Kumar in one and Sunil Kumar in another. Aadhaar is "less ID project and more identification project," says legal researcher Usha Ramanathan. "The onus for ensuring the demographic information is correct falls on the number-holder."

2. Are Aadhaar-enabled Cash Transfers Delivering?

If giving every Indian a unique ID was Aadhaar's main mandate, revamping welfare delivery became its second. In 2011, Nilekani headed a committee to create a roadmap to move to a system of welfare delivery where money was transferred directly into bank accounts of beneficiaries—or direct benefit transfers (DBTs). The architectures it proposed pivoted around Aadhaar and online, realtime biometric authentication. This was to replace the existing smart-card architecture, which can work even in areas even without connectivity.

UIDAI saw the cloud as the future. "We were not very taken with the smart-card solution," says Nadhamuni. "Farmers have to carry multiple smart cards around. And then, there is the cost of the card." Smart-card companies, staring at the prospect of their investments going waste, protested. "Customers and service providers deserve the right to make a convenient choice. Can someone building a public highway insist that only a certain sort of a vehicle can ply on it?" Abhishek Sinha, CEO of Eko India, a mobile-banking start-up told ET in November 2011.

"The question is whether the model is working better now than what existed before," defends Koshy. It's a question that has not been answered conclusively and credibly: there have been no independent evaluations by the government of Aadhaarbased DBTs till now. "Aadhaar should not have been rolled out on a mission mode till it was tested on some scale," says MS Sriram, visiting faculty at IIM Bangalore's Centre for Public Policy. When asked about this, Sen says: "There was no independent evaluation. Everyone was rushing." From the field came reports about manual labourers and the aged struggling to authenticate using biometrics.

Nor were comparative studies conducted to check alternative ways to improve welfare delivery. Economist Reetika Khera argues that Chhattisgarh has removed corruption from its PDS programme through a mix of computerisation and community supervision. This echoes an observation made by the Parliamentary panel while rejecting the UIDAI bill: the government had not considered comparative costs of Aadhaar and other existing ID documents.

Yet, in November 2012, the Congress decided to make DBTs its calling card for the 2014 elections. At a rally in Dudu, Rajasthan, attended by Congress leaders and Nilekani, it announced DBT rollout in the state. A year later, after a patchy rollout, the Congress lost power in the state. And on January 30, the UPA pressed pause on DBTs for cooking gas.

3. Are there Strong Safeguards to Protect a Person's Privacy?

On February 26, the Mumbai High Court directed UIDAI to share its Goa biometrics with the CBI to help it solve a rape case in the state the agency was struggling to solve. UIDAI refused, saying this would violate the privacy of its number holders. The High Court agreed with the CBI. UIDAI went to the Supreme Court, which ruled that its biometric information cannot be shared with any government agency without the consent of number holders.

But the CBI request had shown what could go wrong. "Once you create an ID system, other things happen," says Sen. "The most inevitable one is that government departments—like the police—want to access it. A database exists and I want to use it." Says a Supreme Court lawyer, not wanting to be named: "You innocently give your fingerprints to UIDAI because you want your scholarship or gas subsidy or something. You volunteer this information and then you realise this can be used as evidence against you in a criminal trial?" In time, more agencies will use Aadhaar. "The moment you start putting the Aadhaar number into multiple databases, you make them comparable," says Abraham. "Land registry, tax records, etc, all become comparable." Adds Sen: "We need to think about who can use the authentication service."

He cites the example of banks using Aadhaar to judge a borrower's credit record as a good thing. Conversely, he adds, an insurer using a customer's Aadhaar to access hospital records, and take a call on premiums or policy issuance, is a bad outcome. "Insurance is supposed to work by pooling risk. Should they (insurers) even have the right to ask for authentication?" asks Sen. UIDAI officials say three things in their defence. One, they collect innocuous information, which they don't share. Two, for authentication queries, they only give 'yes/no' answers. Three, they have safeguards.

What is missing is a legal framework that governs collection, use and retention of biometrics. "India has not passed a data privacy law," says Nadhamuni. "This is a very important legislation we need to draft and enact for projects that use large-scale IT systems, be it Aadhaar, NREGA, voter card, income tax, etc. In the absence of such laws, UIDAI came up with rigorous data privacy and security policies to secure resident data." However, the Parliamentary panel, while rejecting the bill, noted that UIDAI began collecting biometric data even as the government worked on a privacy bill and a data protection bill. "The idea that databases can be used by anyone makes people vulnerable, especially in a state where there is neither law nor much respect for law," says Ramanathan.

Aadhaar stands at an uncomfortable junction. A new government, eager to ensure only citizens have unique numbers, could ask all Aadhaar holders to provide address proof and delete the others. Events of the past three months have framed the issues concerning Aadhaar, sometimes with a touch of rhetoric. "This is a good time to open the regulation issue," says Sen.

For more details visit https://cis-india.org/news/economic-times-april-3-2014-m-rajshekhar-should-nandan-nilekani-aadhar-project-for-identity-proof-and-welfare-delivery-exist

The Embodiment of the Right to Privacy within Domestic Legislation

tanvi — 2014-09-08T02:37:39Z

The Right to Privacy is a pivotal construct, essential to the actualization of justice, fairness and equity within any democratic society. It is an instrument used to secure the boundaries of an individual’s personal space, in his interaction with not only the rest of society but also the State.

It is within this realm of the social transaction that there exists an unending conflict between the Right to Privacy of an individual and the overbearing hand of the State as a facilitator of public interest. This right thus acts as a safety valve providing individuals with a sacred space within which their interactions in their personal capacity have no bearing on their conduct in the public sphere. The preservation of this space is incredibly important in order to ensure a willingness of individuals to engage and cooperate with the State in its fulfillment of public welfare measures that would otherwise be deemed as intrusive. It is in this regard that the Right to Privacy, one of the last sustaining rights that an individual holds against a larger State interest, ought to be protected by the law.

There are numerous dimensions to the idea of the Right to Privacy. These include but are not limited to the privacy of person, privacy of communication, personal privacy, transactional privacy, privacy of information and the privacy of personal data.

The Supreme Court of India has come to the rescue of individuals, time and again by construing "Right to Privacy" as an extension of the Fundamental Right to “Protection of Life and Personal liberty” under Article 21 of the Constitution. This has been reflected in the adjudicatory jurisprudence of the Constitutional courts in the country. However, there exists no Constitutional remedy to redress the breach of privacy by a nongovernmental actor, except under tortuous liability. The power and authority of public and private institutions to use an individual’s personal data for larger interests of national security or effectuation of socio-economic policies is still under extensive scrutiny. It is in this regard that we have compiled a number of sectoral legislations, regulating domains ranging from Finance and Telecom to Healthcare, Freedom of Expression, Consumer rights and Procedural codes. The highlighted provisions under each Act pertain to the mechanisms embodied within the legislation for the regulation of privacy within their respective sectors. Through this we aim to determine the threshold for permissible collection of confidential data and regulatory surveillance, provided a sufficient need for the same has been established. The determination of such a threshold is imperative to formulating a consistent and effective regime of privacy protection in India.

Click to download the below resources:

Legislations
Master Circulars
Finance and Privacy
Code of Civil Procedure and Code of Criminal Procedure
Freedom of Expression
Identity and Privacy
National Security and Privacy
Consumer Protection
Transparency and Privacy
Healthcare
Telecom

Case Laws
Code of Civil Procedure and Code of Criminal Procedure
Freedom of Expression
Identity and Privacy
National Security and Privacy
Consumer Protection
Transparency and Privacy
Healthcare
Telecom

For more details visit https://cis-india.org/internet-governance/blog/the-embodiment-of-right-to-privacy-within-domestic-legislation

South African Protection of Personal Information Act, 2013

divij — 2014-05-05T06:59:51Z

As the rapid spread of technology in developing countries allows exponentially increasing availability of and access to personal data through automatic data processing, governments are beginning to recognize the necessity to evolve policies addressing data security and privacy concerns.

The source of pressure for strict legal regulations addressing data protection are both the growing recognition of the importance of privacy rights, as well as the risk of falling behind on international standards on data protection, which would hamper the potential of developing countries as destinations for outsourcing industries which depend largely on processing of information.[1] The Protection of Personal Information Act enacted by South Africa is an example of a policy which enables a comprehensive framework for data security and privacy and is a model for other developing nations which are weighing the costs and benefits of establishing a secure data protection regime.

The South African law traces the right to protection of personal information back to Section 14 of the South African Constitution, which provides for a right against the unlawful collection, retention, dissemination and use of personal information. The law establishes strict restrictions and regulations on the processing of personal information, which includes information including relating to race, gender, sexual orientation, medical information, biometric information and personal opinion. The processing of personal information under the Act must comply with 8 principles, namely - accountability, lawful purpose for processing and processing limitation, purpose specification, information quality, openness and notice of collection, openness, reasonable security safeguards and subject participation, in line with the international standards for fair information practices.[2] The Act also recognizes ‘special personal information’, including religious or political beliefs, race, sexual orientation and trade union membership, as well as any personal information of children below the age of 18, which require stricter safeguards for processing,. Similar to the draft Indian legislation on privacy, the Act contemplates an independent regulatory mechanism, the information regulator, which would have all the necessary powers to effectively monitor compliance under the Act, including the power for punishing offences under the Act.

The Protection of Personal Information Act contains 115 Sections and is meant to be an exhaustive and heavily detailed policy to bring South Africa’s laws in line with EU and international regulations on data protection.[3] Though such progressive policies should be a model for policy changes in other developing nations, one aspect in which the law fails is to address increasing privacy concerns arising from widespread government-enabled surveillance and data retention. The POPI excludes from its application the processing of information related to national security, terrorist related activities and public safety, combating of money laundering, investigation of proof of offences, the prosecution of offenders, execution of sentences or other security measures, subject to adequate safeguards being established by the legislature for protection of personal information. Unfortunately, the ambiguous wording of the exclusions, especially in determining “adequate safeguards”, leaves its interpretation and application open for governments to engage in mass surveillance in the name of public security. Over the past few years, governments have taken to using technology and information, particularly through mass surveillance, to collect comprehensive information on their citizens and violate their liberties and privacy. In India, particularly with programs like the Central Monitoring System being implemented, any policy which purportedly aims at the protection of privacy must not only seek bare minimal compliances with the current international standards for data protection, but should also address the mass, unrestricted surveillance and data retention which is taking place in the name of public security.

Developing nations like South Africa and India face significant challenges in ensuring individual privacy, particularly the lack of sufficient legal safeguards for the protection of privacy. The right to privacy is often dismissed as an elitist or western concept, which does not have value in the context of developing nations, without engaging with the realities and the nuances of the right. Further, the costs of expensive technical safeguards means private and public bodies are required to spend significant resources in maintaining data security and these factors often outweigh privacy considerations in policy debates. The South African Act, hence, serves both as an important model for legislation and as an indication that the right to privacy is valuable to recognize in developing countries as well.

[1]. Article 25 of the European Union Directive on the Protection of Individuals with regard to the Processing of Personal Data and on the Free Movement of such data (Directive 95/46/EC) prohibits the transfer of data to non-member states which do not comply with adequate data protection norms.

[2]. http://oecdprivacy.org/

[3]. Link to Act: www.gov.za/documents/download.php?f=204368

For more details visit https://cis-india.org/internet-governance/blog/south-african-protection-personal-information-act-2013

Civil Society Pushes for Privacy Panel

praskrishna — 2014-05-27T11:39:20Z

The article was published in the Times of India on May 6, 2014. Sunil Abraham is quoted.

Civil society organizations are pushing for a 'privacy commission' to provide protection to individuals from illegal breach of their privacy, with guidelines imposing penal sanction against the violators. This assumes significance

This assumes significance at a time when the Centre has decided to set up a judicial panel to probe the snoopgate scandal wherein the BJP government in Gujarat was allegedly involved in illegal surveillance of a woman architect and especially when the Right to Privacy Bill is pending in Parliament.

However, industry consortia, including CII and FICCI, prefer lesser regulation, though calling for a cautious approach.

Among civil society organizations pressing for a stringent privacy bill is the International Centre for Free and Open Source Software (ICFOSS), the only representative from Kerala to attend the NETmundial conference held recently in Brazil. The meet focused on privacy issues to ensure basic human rights, including freedom of expression.

NETmundial is the first step towards pushing for a privacy law against the snooping and spying on individuals by those in power, including agencies within and outside the country Privacy guidelines should be clear as to what data can be collected without infringing on the dignity of an individual as 'data' represents the duration of a call, while 'metadata' reveals the content of the caH," said ICFOSS director SatishBabu.

Bangalore-based Centre for Internet and Society (CIS), another NETmundial participant, also stands for a strong privacy law. "The two-day conference that concluded on April 24 was a baby step towards a privacy law with a road map for global internet governance. It is the first step towards a multi-stakeholder model offering an equal footing for all civil society organizations, academia, government, private sector and the UN fora," said CIS executive director Sunil Abraham

“We are pushing for a privacy law in the country aimed at national privacy regulation and constituting a privacy commission on the lines of the information commission," he added.

Click to read the full story

For more details visit https://cis-india.org/news/the-times-of-india-may-6-2014-laxmi-ajai-prasanna-civil-society-pushes-for-privacy-panel

Use of Visuals and Nudges in Privacy Notices

saumyaa — 2018-08-22T13:16:15Z

Nudging in privacy notices can be a privacy-enhancing tool. For example, informing users of how many people would have access to their data would help them make a decision. However, nudges can also be used to influence users towards making choices that compromise their privacy. For example, the visual design of default options on digital platforms currently nudge users to share their data. It is critical to ensure that there is mindful use of nudges, and that it is directed at the well being of the users.

Edited by Elonnai Hickok and Amber Sinha

Former Supreme Court judge, Justice B.N. Srikrishna, who is currently involved in drafting the new data-privacy laws for India, was quoted recently by the Bloomberg^[1]. Acknowledging the ineffectiveness of consent forms of tech companies that leads to users’ data being collected and misused, he asked if we should have pictograph warnings for consent much like the warnings that are given on cigarette packets. His concern is that an average Indian does not realise how much data they are generating or how it is being used. He attributed this to the access issues with the consent forms presented by companies which are in the English language. In the Indian context, Justice Srikrishna pointed out, considerations around literacy and languages should be addressed.

The new framework being worked on by Srikrishna and his committee comprising academics and government officials, would make the tech companies more accountable for data collection and use, and allow users to have more control over their own data. But, in addition to this regulatory step towards privacy and data protection, the concern towards communication of companies’ data practices through consent forms or privacy notices is also critical for users. Currently, the cryptic notices are a barrier for users, as are the services that do not provide incremental information about the use of the service - for example, what data is being shared with how many people or what data is being collected at what point, instead relying on blanket consent forms taken at the beginning of a service. Visuals can go a long way in making these notices and services accessible to users.

Although, Justice Srikrishna chose the extreme example of warnings on cigarette packets, visually depicting the health risks of cigarette smoking using repulsive imagery, the underlying intent seems to be of using visuals as a means of giving an immediate and clear warning about how people’s data is being used and by whom. It must be noted that the effectiveness of warnings on cigarette packets is debatable. These warnings are also a way in which manufacturers consider their accountability met, which is a possible danger with privacy notices as well. Most companies consider that their accountability is limited to giving all the information to the users without ensuring that the information is communicated to help the user understand the risks. Hence, one has to be cautious of the role of visuals in notices so that they are used with the primary purpose of meaningful communication and accessibility that can be used to inform further action. The visual summary of the data practice in terms of how it will affect the user will also serve as a warning.

The warning images on cigarette packets are an example of the user-influencing design approach called nudging^{^[2]}. While nudging techniques are meant to be aimed at the users’ well being, it brings forward the question of who decides what is beneficial for the users. Moreover, the harm in cigarette smoking is more obvious, and thus the favourable choice for the users is also clearer. But, in the context of data privacy, the harms are less apparent. It is difficult to demonstrate the harms or benefits of data use, particularly when data is re-purposed or used indirectly. There is also no single choice that can be pushed when it comes to the use and collection of data. Different users may have different preferences or degrees to which they would like to allow the use of their data. This raises deeper questions about the extent to which privacy law and regulation should be paternalistic.

Nudges are considered to follow the soft or libertarian paternalism approach, where the user is not forbidden any options but only given a push to alter their behaviour in a predictable way^{^[3]}. It is crucial to differentiate between the strong paternalistic approach that doesn’t allow a choice at all, the usability approach, and the soft paternalistic approach of nudging, as mentioned by Alessandro Acquisti in his paper, ‘The Behavioral Economics of Personal Information’^{^[4]}. In the usability approach, the design of the system would make it intuitive for users to change settings and secure their data. The soft paternalistic approach of nudging would be a step further and present secure settings as a default. Usability is often prioritised by designers. However, soft paternalism techniques help to enhance choice for users and lead to larger welfare^{^[5]}.

Nudging in privacy notices can be a privacy-enhancing tool. For example, informing users of how many people would have access to their data would help them make a decision^{^[6]}. However, nudges can also be used to influence users towards making choices that compromise their privacy. For example, the visual design of default options on digital platforms currently nudge users to share their data. It is critical to ensure that there is mindful use of nudges, and that it is directed at the well being of the users.

The design of privacy notices should be re-conceptualised to ensure that they inform the users effectively, keeping in mind certain best practices. For instance, a multilayered privacy notice can be used, which includes a very short notice designed for use on portable digital devices where there is limited space, condensed notice that contains all the key factors in an easy to understand way, and a complete notice with all the legal requirements^{^[7]}. Along with the layering of information, the timing of notices should also be designed to be at setup, just in time of the user’s action, or at periodic intervals. In terms of visuals, infographics can be used to depict data flows in a system. Another best practice is to integrate privacy notices with the rest of the system. Designers are needed to be involved early in the process so that the design decisions are not purely visual but also consider information architecture, content design, and research.

Practice based frameworks should be developed for communication designers in order to have a standardised vocabulary around creating privacy notices. Additionally, multiple user groups and their varied privacy preferences must be taken into account. Finally, an ethical framework must be put into place for design practitioners in order to ensure that the users’ well being is prioritised, and notices are designed to facilitate informed consent. Further recommendations and concerns regarding the design of privacy notices, and the use of visuals can be read here.

Justice Srikrishna’s statement is an important step towards creating effective privacy notices with visuals. The conversation on the need to design privacy notices can lead to clearer and more comprehensible notices. Combined with the enforcement of fair collection and use of data by companies, well designed notices will allow users more control and a real choice to opt-in or out of a service and make informed choices as they engage with a service. Justice Srikrishna’s analogy seems to recommend using visuals to describe what type of data is being collected and for what purposes at the time of taking consent. Though cigarette warnings may not be the most appropriate analogy, this is a good start, and it is important to explore how visuals and design can be used throughout a service - from beginning to end - to convey and promote awareness and informed choices by users. It is also important to extend this conversation outside of privacy into the realm of security and understand how visuals and design can inform users’ awareness and personal choices around security when using a service.

^{^[1]} https://www.bloomberg.com/news/articles/2018-06-10/tech-giants-nervous-as-judge-drafts-first-data-rules-in-india

^{^[2]} http://www.ijdesign.org/index.php/IJDesign/article/viewFile/1512/584

^{^[3]} https://www.andrew.cmu.edu/user/pgl/psosm2013.pdf

^{^[4]} https://www.heinz.cmu.edu/~acquisti/papers/acquisti-privacy-nudging.pdf

^{^[5]} https://www.heinz.cmu.edu/~acquisti/papers/acquisti-privacy-nudging.pdf

^{^[6]} https://cis-india.org/internet-governance/files/rethinking-privacy-principles

^{^[7]} https://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/ten_steps_to_develop_a_multilayered_privacy_notice__white_paper_march_2007_.pdf

For more details visit https://cis-india.org/internet-governance/blog/use-of-visuals-and-nudges-in-privacy-notices

AI in India: A Policy Agenda

Amber Sinha, Elonnai Hickok and Arindrajit Basu — 2018-09-05T15:39:59Z

Click to download the file

Background

Over the last few months, the Centre for Internet and Society has been engaged in the mapping of use and impact of artificial intelligence in health, banking, manufacturing, and governance sectors in India through the development of a case study compendium.^{^[1]} Alongside this research, we are examining the impact of Industry 4.0 on jobs and employment and questions related to the future of work in India. We have also been a part of several global conversations on artificial intelligence and autonomous systems. The Centre for Internet and Society is part of the Partnership on Artificial Intelligence, a consortium which has representation from some of most important companies and civil society organisations involved in developments and research on artificial intelligence. We have contributed to the The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, and are also a part of a Big Data for Development Global Network, where we are undertaking research towards evolving ethical principles for use of computational techniques. The following are a set of recommendations we have arrived out of our research into artificial intelligence, particularly the sectoral case studies focussed on the development and use of artificial intelligence in India.

National AI Strategies: A Brief Global Overview

Artificial Intelligence is emerging as a central policy issue in several countries. In October 2016, the Obama White House released a report titled, “Preparing for the Future of Artificial Intelligence”^{^[2]} delving into a range of issues including application for public goods, regulation, economic impact, global security and fairness issues. The White House also released a companion document called the “National Artificial Intelligence Research and Development Strategic Plan”^{^[3]} which laid out a strategic plan for Federally-funded research and development in AI. These were the first of a series of policy documents released by the US towards the role of AI. The United Kingdom announced its 2020 national development strategy and issued a government report to accelerate the application of AI by government agencies while in 2018 the Department for Business, Energy, and Industrial Strategy released the Policy Paper - AI Sector Deal.^{^[4]} The Japanese government released it paper on Artificial Intelligence Technology Strategy in 2017.^{^[5]} The European Union launched "SPARC," the world’s largest civilian robotics R&D program, back in 2014.^{^[6]}

Over the last year and a half, Canada,^{^[7]} China,^{^[8]} the UAE,^{^[9]} Singapore,^{^[10]} South Korea^{^[11]}, and France^{^[12]} have announced national AI strategy documents while 24 member States in the EU have committed to develop national AI policies that reflect a “European” approach to AI ^{^[13]}. Other countries such as Mexico and Malaysia are in the process of evolving their national AI strategies. What this suggests is that AI is quickly emerging as central to national plans around the development of science and technology as well as economic and national security and development. There is also a focus on investments enabling AI innovation in critical national domains as a means of addressing key challenges facing nations. India has followed this trend and in 2018 the government published two AI roadmaps - the Report of Task Force on Artificial Intelligence by the AI Task Force constituted by the Ministry of Commerce and Industry^{^[14]} and the National Strategy for Artificial Intelligence by Niti Aayog.^{^[15]} Some of the key themes running across the National AI strategies globally are spelt out below.

Economic Impact of AI

A common thread that runs across the different national approaches to AI is the belief in the significant economic impact of AI, that it will likely increase productivity and create wealth. The British government estimated that AI could add $814 billion to the UK economy by 2035. The UAE report states that by 2031, AI will help boost the country’s GDP by 35 per cent, reduce government costs by 50 per cent. Similarly, China estimates that the core AI market will be worth 150 billion RMB ($25bn) by 2020, 400 billion RMB ($65bn) and one trillion RMB ($160bn) by 2030. The impact of adoption of AI and automation of labour and employment is also a key theme touched upon across the strategies. For instance, the White House Report of October 2016 states the US workforce is unprepared – and that a serious education programme, through online courses and in-house schemes, will be required.^{^[16]}

State Funding

Another key trend exhibited in all national strategies towards AI has been a commitment by the respective governments towards supporting research and development in AI. The French government has stated that it intends to invest €1.5 billion ($1.85 billion) in AI research in the period through to 2022. The British government’s recommendations, in late 2017, were followed swiftly by a promise in the autumn budget of new funds, including at least £75 million for AI. Similarly, the the Canadian government put together a $125-million ‘pan-Canadian AI strategy’ last year.

AI for Public Good

The use of AI for Public Good is a significant focus of most AI policies. The biggest justification for AI innovation as a legitimate objective of public policy is its promised impact towards improvement of people’s lives by helping to solve some of the world’s greatest challenges and inefficiencies, and emerge as a transformative technology, much like mobile computing. These public good uses of AI are emerging across sectors such as transportation, migration, law enforcement and justice system, education, and agriculture..

National Institutions leading AI research

Another important trend which was key to the implementation of national AI strategies is the creation or development of well-funded centres of excellence which would serve as drivers of research and development and leverage synergies with the private sector. The French Institute for Research in Computer Science and Automation (INRIA) plans to create a national AI research program with five industrial partners. In UK, The Alan Turing Institute is likely to emerge as the national institute for data science, and an AI Council would be set up to manage inter-sector initiatives and training. In Canada, Canadian Institute for Advanced Research (CIFAR) has been tasked with implementing their AI strategy. Countries like Japan has a less centralised structure with the creation of strategic council for AI technology’ to promote research and development in the field, and manage a number of key academic institutions, including NEDO and its national ICT (NICT) and science and tech (JST) agencies. These institutions are key to successful implementation of national agendas and policies around AI.

AI, Ethics and Regulation

Across the AI strategies — ethical dimensions and regulation of AI were highlighted as concerns that needed to be addressed. Algorithmic transparency and explainability, clarity on liability, accountability and oversight, bias and discrimination, and privacy are ethical and regulatory questions that have been raised. Employment and the future of work is another area of focus that has been identified by countries. For example, the US 2016 Report reflected on if existing regulation is adequate to address risk or if adaption is needed by examining the use of AI in automated vehicles. In the policy paper - AI Sector Deal - the UK proposes four grand challenges: AI and Data Economy, Future Mobility, Clean Growth, and Ageing Society. The Pan Canadian Artificial Intelligence Strategy focuses on developing global thought leadership on the economic, ethical, policy, and legal implications of advances in artificial intelligence.^{^[17]}

The above are important factors and trends to take into account and to different extents have been reflected in the two national roadmaps for AI. Without adequate institutional planning, there is a risk of national strategies being too monolithic in nature. Without sufficient supporting mechanisms in the form of national institutions which would drive the AI research and innovation, capacity building and re-skilling of workforce to adapt to changing technological trends, building regulatory capacity to address new and emerging issues which may disrupt traditional forms of regulation and finally, creation of an environment of monetary support both from the public and private sector it becomes difficult to implement a national strategy and actualize the potentials of AI . As stated above, there is also a need for identification of key national policy problems which can be addressed by the use of AI, and the creation of a framework with institutional actors to articulate the appropriate plan of action to address the problems using AI. There are several ongoing global initiatives which are in the process of trying to articulate key principles for ethical AI. These discussions also feature in some of the national strategy documents.

Key considerations for AI policymaking in India

As mentioned above, India has published two national AI strategies. We have responded to both of these here^{^[18]} and here.^{^[19]} Beyond these two roadmaps, this policy brief reflects on a number of factors that need to come together for India to leverage and adopt AI across sectors, communities, and technologies successfully.

Resources, Infrastructure, Markets, and Funding

Ensure adequate government funding and investment in R&D

As mentioned above, a survey of all major national strategies on AI reveals a significant financial commitment from governments towards research and development surrounding AI. Most strategy documents speak of the need to safeguard national ambitions in the race for AI development. In order to do so it is imperative to have a national strategy for AI research and development, identification of nodal agencies to enable the process, and creation of institutional capacity to carry out cutting edge research.

Most jurisdictions such as Japan, UK and China have discussed collaborations between the industry and government to ensure greater investment into AI research and development. The European Union has spoken using the existing public-private partnerships, particularly in robotics and big data to boost investment by over one and half times.^{^[20]} To some extent, this step has been initiated by the Niti Aayog strategy paper. The paper lists out enabling factors for the widespread adoption of AI and maps out specific government agencies and ministries that could promote such growth. In February 2018, the Ministry of Electronics and IT also set up four committees to prepare a roadmap for a national AI programme. The four committees are presently studying AI in context of citizen centric services; data platforms; skilling, reskilling and R&D; and legal, regulatory and cybersecurity perspectives.^{^[21]}

Democratize AI technologies and data

Clean, accurate, and appropriately curated data is essential for training algorithms. Importantly, large quantities of data alone does not translate into better results. Accuracy and curation of data should be prerequisites to quantity of data. Frameworks to generate and access larger quantity of data should not hinge on models of centralized data stores. The government and the private sector are generally gatekeepers to vast amounts of data and technologies. Ryan Calo has called this an issue of data parity,^{^[22]} where only a few well established leaders in the field have the ability to acquire data and build datasets. Gaining access to data comes with its own questions of ownership, privacy, security, accuracy, and completeness. There are a number of different approaches and techniques that can be adopted to enable access to data.

Open Government Data

Robust open data sets is one way in which access can be enabled. Open data is particularly important for small start-ups as they build prototypes. Even though India is a data dense country and has in place a National Data and Accessibility Policy India does not yet have robust and comprehensive open data sets across sectors and fields. Our research found that this is standing as an obstacle to innovation in the Indian context as startups often turn to open datasets in the US and Europe for developing prototypes. Yet, this is problematic because the demography represented in the data set is significantly different resulting in the development of solutions that are trained to a specific demographic, and thus need to be re-trained on Indian data. Although AI is technology agnostic, in the cases of different use cases of data analysis, demographically different training data is not ideal. This is particularly true for certain categories such as health, employment, and financial data.

The government can play a key role in providing access to datasets that will help the functioning and performance of AI technologies. The Indian government has already made a move towards accessible datasets through the Open Government Data Platform which provides access to a range of data collected by various ministries. Telangana has developed its own Open Data Policy which has stood out for its transparency and the quality of data collected and helps build AI based solutions.

In order to encourage and facilitate innovation, the central and state governments need to actively pursue and implement the National Data and Accessibility Policy.

Access to Private Sector Data

The private sector is the gatekeeper to large amounts of data. There is a need to explore different models of enabling access to private sector data while ensuring and protecting users rights and company IP. This data is often considered as a company asset and not shared with other stakeholders. Yet, this data is essential in enabling innovation in AI.

Amanda Levendowski states that ML practitioners have essentially three options in securing sufficient data— build the databases themselves, buy the data, or use data in the public domain. The first two alternatives are largely available to big firms or institutions. Smaller firms often end resorting to the third option but it carries greater risks of bias.

A solution could be federated access, with companies allowing access to researchers and developers to encrypted data without sharing the actual data. Another solution that has been proposed is ‘watermarking’ data sets.

Data sandboxes have been promoted as tools for enabling innovation while protecting privacy, security etc. Data sandboxes allow companies access to large anonymized data sets under controlled circumstances. A regulatory sandbox is a controlled environment with relaxed regulations that allow the product to be tested thoroughly before it is launched to the public. By providing certification and safe spaces for testing, the government will encourage innovation in this sphere. This system has already been adopted in Japan where there are AI specific regulatory sandboxes to drive society 5.0.160 data sandboxes are tools that can be considered within specific sectors to enable innovation. A sector wide data sandbox was also contemplated by TRAI.^{^[23]} A sector specific governance structure can establish a system of ethical reviews of underlying data used to feed the AI technology along with data collected in order to ensure that this data is complete, accurate and has integrity. A similar system has been developed by Statistics Norway and the Norwegian Centre for Research Data.^{^[24]}

AI Marketplaces

The National Roadmap for Artificial Intelligence by NITI Aayog proposes the creation of a National AI marketplace that is comprised of a data marketplace, data annotation marketplace, and deployable model marketplace/solutions marketplace.^{^[25]} In particular, it is envisioned that the data marketplace would be based on blockchain technology and have the features of: traceability, access controls, compliance with local and international regulations, and robust price discovery mechanism for data. Other questions that will need to be answered center around pricing and ensuring equal access. It will also be interesting how the government incentivises the provision of data by private sector companies. Most data marketplaces that are emerging are initiated by the private sector.^{^[26]} A government initiated marketplace has the potential to bring parity to some of the questions raised above, but it should be strictly limited to private sector data in order to not replace open government data.

Open Source Technology

A number of companies are now offering open source AI technologies. For example, TensorFlow, Keras, Scikit-learn, Microsoft Cognitive Toolkit, Theano, Caffe, Torch, and Accord.NET.^{^[27]} The government should incentivise and promote open source AI technologies towards harnessing and accelerating research in AI.

Re-thinking Intellectual Property Regimes

Going forward it will be important for the government to develop an intellectual property framework that encourages innovation. AI systems are trained by reading, viewing, and listening to copies of human-created works. These resources such as books, articles, photographs, films, videos, and audio recordings are all key subjects of copyright protection. Copyright law grants exclusive rights to copyright owners, including the right to reproduce their works in copies, and one who violates one of those exclusive rights “is an infringer of copyright.^{^[28]}

The enterprise of AI is, to this extent, designed to conflict with tenets of copyright law, and after the attempted ‘democratization’ of copyrighted content by the advent of the Internet, AI poses the latest challenge to copyright law. At the centre of this challenge is the fact that it remains an open question whether a copy made to train AI is a “copy” under copyright law, and consequently whether such a copy is an infringement.^{^[29]} The fractured jurisprudence on copyright law is likely to pose interesting legal questions with newer use cases of AI. For instance, Google has developed a technique called federated learning, popularly referred to as on-device ML, in which training data is localised to the originating mobile device rather than copying data to a centralized server.^{^[30]} The key copyright questions here is whether decentralized training data stored in random access memory (RAM) would be considered as “copies”.^{^[31]} There are also suggestions that copies made for the purpose of training of machine learning systems may be so trivial or de minimis that they may not qualify as infringement.^{^[32]} For any industry to flourish, there needs to be legal and regulatory clarity and it is imperative that these copyright questions emerging out of use of AI be addressed soon.

As noted in our response to the Niti Aayog national AI strategy “The report also blames the current Indian Intellectual Property regime for being “unattractive” and averse to incentivising research and adoption of AI. Section 3(k) of Patents Act exempts algorithms from being patented, and the Computer Related Inventions (CRI) Guidelines have faced much controversy over the patentability of mere software without a novel hardware component. The paper provides no concrete answers to the question of whether it should be permissible to patent algorithms, and if yes, to to what extent. Furthermore, there needs to be a standard either in the CRI Guidelines or the Patent Act, that distinguishes between AI algorithms and non-AI algorithms. Additionally, given that there is no historical precedence on the requirement of patent rights to incentivise creation of AI, innovative investment protection mechanisms that have lesser negative externalities, such as compensatory liability regimes would be more desirable. The report further failed to look at the issue holistically and recognize that facilitating rampant patenting can form a barrier to smaller companies from using or developing AI. This is important to be cognizant of given the central role of startups to the AI ecosystem in India and because it can work against the larger goal of inclusion articulated by the report.”^{^[33]}

National infrastructure to support domestic development

Building a robust national Artificial Intelligence solution requires establishing adequate indigenous infrastructural capacity for data storage and processing. While this should not necessarily extend to mandating data localisation as the draft privacy bill has done, capacity should be developed to store data sets generated by indigenous nodal points.

AI Data Storage

Capacity needs to increase as the volume of data that needs to be processed in India increases. This includes ensuring effective storage capacity, IOPS (Input/Output per second) and ability to process massive amounts of data.

AI Networking Infrastructure

Organizations will need to upgrade their networks in a bid to upgrade and optimize efficiencies of scale. Scalability must be undertaken on a high priority which will require a high-bandwidth, low latency and creative architecture, which requires appropriate last mile data curation enforcement.

Conceptualization and Implementation

Awareness, Education, and Reskilling

Encouraging AI research

This can be achieved by collaborations between the government and large companies to promote accessibility and encourage innovation through greater R&D spending. The Government of Karnataka, for instance, is collaborating with NASSCOM to set up a Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI) on a public-private partnership model to “accelerate the ecosystem in Karnataka by providing the impetus for the development of data science and artificial intelligence across the country.” Similar centres could be incubated in hospitals and medical colleges in India. Principles of public funded research such as FOSS, open standards, and open data should be core to government initiatives to encourage research. The Niti Aaayog report proposes a two tier integrated approach towards accelerating research, but is currently silent on these principles.^{^[34]}

Therefore,as suggested by the NITI AAYOG Report, the government needs to set up ‘centres of excellence’. Building upon the stakeholders identified in the NITI AAYOG Report, the centers of excellence should involve a wide range of experts including lawyers, political philosophers, software developers, sociologists and gender studies from diverse organizations including government, civil society,the private sector and research institutions to ensure the fair and efficient roll out of the technology.^{^[35]} An example is the Leverhulme Centre for the Future of Intelligence set up by the Leverhulme Foundation at the University of Cambridge^{^[36]} and the AI Now Institute at New York University (NYU)^{^[37]} These research centres bring together a wide range of experts from all over the globe.^{^[38]}

Skill sets to successfully adopt AI

Educational institutions should provide opportunities for students to skill themselves to adapt to adoption of AI, and also push for academic programmes around AI. It is also important to introduce computing technologies such as AI in medical schools in order to equip doctors to adopt the technical skill sets and ethics required to use integrate AI in their practices. Similarly, IT institutes could include courses on ethics, privacy, accountability etc. to equip engineers and developers with an understanding of the questions surrounding the technology and services they are developing.

Societal Awareness Building

Much of the discussion around skilling for AI is in the context of the workplace, but there is a need for awareness to be developed across society for a broader adaptation to AI. The Niti Aayog report takes the first steps towards this - noting the importance of highlighting the benefits of AI to the public. The conversation needs to go beyond this towards enabling individuals to recognize and adapt to changes that might be brought about - directly and indirectly - by AI - inside and outside of the workplace. This could include catalyzing a shift in mindset to life long learning and discussion around potential implications of human-machine interactions.

Early Childhood Awareness and Education

It is important that awareness around AI begins in early childhood. This is in part because children already interact with AI and increasingly will do so and thus awareness is needed in how AI works and can be safely and ethically used. It is also important to start building the skills that will be necessary in an AI driven society from a young age.

Focus on marginalised groups

Awareness, skills, and education should be targeted at national minorities including rural communities, the disabled, and women. Further, there should be a concerted focus on communities that are under-represented in the tech sector-such as women and sexual minorities-to ensure that the algorithms themselves and the community working on AI driven solutions are holistic and cohesive. For example, Iridescent focuses on girls, children, and families to enable them to adapt to changes like artificial intelligence through promoting curiosity, creativity, and perseverance to become lifelong learners.^{^[39]} This will be important towards ensuring that AI does not deepen societal and global inequalities including digital divides. Widespread use of AI will undoubtedly require re-skilling various stakeholders in order to make them aware of the prospects of AI.^{^[40]} Artificial Intelligence itself can be used as a resource in the re-skilling process itself-as it would be used in the education sector to gauge people’s comfort with the technology and plug necessary gaps.

Improved access to and awareness of Internet of Things

The development of smart content or Intelligent Tutoring Systems in the education can only be done on a large scale if both the teacher and the student has access to and feel comfortable with using basic IoT devices . A U.K. government report has suggested that any skilled workforce using AI should be a mix of those with a basic understanding responsible for implementation at the grassroots level , more informed users and specialists with advanced development and implementation skills.^{^[41]}The same logic applies to the agriculture sector, where the government is looking to develop smart weather-pattern tracking applications. A potential short-term solution may lie in ensuring that key actors have access to an IoT device so that he/she may access digital and then impart the benefits of access to proximate individuals. In the education sector, this would involve ensuring that all teachers have access to and are competent in using an IoT device. In the agricultural sector, this may involve equipping each village with a set of IoT devices so that the information can be shared among concerned individuals. Such an approach recognizes that AI is not the only technology catalyzing change - for example industry 4.0 is understood as comprising of a suite of technologies including but not limited to AI.

Public Discourse

As solutions bring together and process vast amounts of granular data, this data can be from a variety of public and private sources - from third party sources or generated by the AI and its interaction with its environment. This means that very granular and non traditional data points are now going into decision making processes. Public discussion is needed to understand social and cultural norms and standards and how these might translate into acceptable use norms for data in various sectors.

Coordination and collaboration across stakeholders

Development of Contextually Nuanced and Appropriate AI Solutions

Towards ensuring effectiveness and accuracy it is important that solutions used in India are developed to account for cultural nuances and diversity. From our research this could be done in a number of ways ranging from: training AI solutions used in health on data from Indian patients to account for differences in demographics^{^[42]}, focussing on natural language voice recognition to account for the diversity in languages and digital skills in the Indian context,^{^[43]} and developing and applying AI to reflect societal norms and understandings.^{^[44]}

Continuing, deepening, and expanding partnerships for innovation

Continued innovation while holistically accounting for the challenges that AI poses will be key for actors in the different sectors to remain competitive. As noted across case study reports partnerships is key in facilitating this innovation and filling capacity gaps. These partnerships can be across sectors, institutions, domains, geographies, and stakeholder groups. For example: finance/ telecom, public/private, national/international, ethics/software development/law, and academia/civil society/industry/government. We would emphasize collaboration between actors across different domains and stakeholder groups as developing holistics AI solutions demands multiple understandings and perspectives.

Coordinated Implementation

Key sectors in India need to begin to take steps to consider sector wide coordination in implementing AI. Potential stress and system wide vulnerabilities would need to be considered when undertaking this. Sectoral regulators such as RBI, TRAI, and the Medical Council of India are ideally placed to lead this coordination.

Develop contextual standard benchmarks to assess quality of algorithms

In part because of the nacency of the development and implementation of AI, towards enabling effective assessments of algorithms to understand impact and informing selection by institutions adopting solutions, standard benchmarks can help in assessing quality and appropriateness of algorithms. It may be most effective to define such benchmarks at a sectoral level (finance etc.) or by technology and solution (facial recognition etc.). Ideally, these efforts would be led by the government in collaboration with multiple stakeholders.

Developing a framework for working with the private sector for use-cases by the government

There are various potential use cases the government could adopt in order to use AI as a tool for augmenting public service delivery in India by the government. However, given lack of capacity -both human resource and technological-means that entering into partnerships with the private sector may enable more fruitful harnessing of AI- as has been seen with existing MOUs in the agricultural^{^[45]} and healthcare sectors.^{^[46]} However, the partnership must be used as a means to build capacity within the various nodes in the set-up rather than relying only on the private sector partner to continue delivering sustainable solutions.

Particularly, in the case of use of AI for governance, there is a need to evolve a clear parameter to do impact assessment prior to the deployment of the technology that clearly tries to map estimated impact of the technology of clearly defined objectives, which must also include the due process, procedural fairness and human rights considerations . As per Article 12 of the Indian Constitution, whenever the government is exercising a public function, it is bound by the entire gamut of fundamental rights articulated in Part III of the Constitution. This is a crucial consideration the government will have to bear in mind whenever it uses AI-regardless of the sector. In all cases of public service delivery, primary accountability for the use of AI should lie with the government itself, which means that a cohesive and uniform framework which regulates these partnerships must be conceptualised. This framework should incorporate : (a) Uniformity in the wording and content of contracts that the government signs, (b) Imposition of obligations of transparency and accountability on the developer to ensure that the solutions developed are in conjunction with constitutional standards and (c) Continuous evaluation of private sector developers by the government and experts to ensure that they are complying with their obligations.

Defining Safety Critical AI

The implications of AI differs according to use. Some countries, such as the EU, are beginning to define sectors where AI should play the role of augmenting jobs as opposed to functioning autonomously. The Global Partnership on AI is has termed sectors where AI tools supplement or replace human decision making in areas such as health and transportation as ‘safety critical AI’ and is researching best practices for application of AI in these areas. India will need to think through if there is a threshold that needs to be set and more stringent regulation applied. In addition to uses in health and transportation, defense and law enforcement would be another sector where certain use would require more stringent regulation.

Appropriate certification mechanisms

Appropriate certificate mechanisms will be important in ensuring the quality of AI solutions. A significant barrier to the adoption of AI in some sectors in India is acceptability of results, which include direct results arrived at using AI technologies as well as opinions provided by practitioners that are influenced/aided by AI technologies. For instance, start-ups in the healthcare sectors often find that they are asked to show proof of a clinical trial when presenting their products to doctors and hospitals, yet clinical trials are expensive, time consuming and inappropriate forms of certification for medical devices and digital health platforms. Startups also face difficulty in conducting clinical trials as there is lack of a clear regulation to adhere to. They believe that while clinical trials are a necessity with respect to drugs, the process often results in obsolescence of the technology by the time it is approved in the context of AI. Yet, medical practitioners are less trusting towards startups who do not have approval from a national or international authority. A possible and partial solution suggested by these startups is to enable doctors to partner with them to conduct clinical trials together. However, such partnerships cannot be at the expense of rigour, and adequate protections need to be built in the enabling regulation.

Serving as a voice for emerging economies in the global debate on AI

While India should utilise Artificial Intelligence in the economy as a means of occupying a driving role in the global debate around AI, it must be cautious before allowing the use of Indian territory and infrastructure as a test bed for other emerging economies without considering the ramifications that the utilisation of AI may have for Indian citizens. The NITI AAYOG Report envisions India as leverage AI as a ‘garage’ for emerging economies.^{^[47]} While there are certain positive connotations of this suggestion in so far as this propels India to occupy a leadership position-both technically and normatively in determining future use cases for AI in India,, in order to ensure that Indian citizens are not used as test subjects in this process, guiding principles could be developed such as requiring that projects have clear benefits for India.

Frameworks for Regulation

National legislation

Data Protection Law

India is a data-dense country, and the lack of a robust privacy regime, allows the public and private sector easier access to large amounts of data than might be found in other contexts with stringent privacy laws. India also lacks a formal regulatory regime around anonymization. In our research we found that this gap does not always translate into a gap in practice, as some start up companies have adopted self-regulatory practices towards protecting privacy such as of anonymising data they receive before using it further, but it does result in unclear and unharmonized practice..

In order to ensure rights and address emerging challenges to the same posed by artificial intelligence, India needs to enact a comprehensive privacy legislation applicable to the private and public sector to regulate the use of data, including use in artificial intelligence. A privacy legislation will also have to address more complicated questions such as the use of publicly available data for training algorithms, how traditional data categories (PI vs. SPDI - meta data vs. content data etc.) need to be revisited in light of AI, and how can a privacy legislation be applied to autonomous decision making. Similarly, surveillance laws may need to be revisited in light of AI driven technologies such as facial recognition, UAS, and self driving cars as they provide new means of surveillance to the state and have potential implications for other rights such as the right to freedom of expression and the right to assembly. Sectoral protections can compliment and build upon the baseline protections articulated in a national privacy legislation.^{^[48]} In August 2018 the Srikrishna Committee released a draft data protection bill for India. We have reflected on how the Bill addresses AI. Though the Bill brings under its scope companies deploying emerging technologies and subjects them to the principles of privacy by design and data impact assessments, the Bill is silent on key rights and responsibilities, namely the responsibility of the data controller to explain the logic and impact of automated decision making including profiling to data subjects and the right to opt out of automated decision making in defined circumstances.^{^[49]} Further, the development of technological solutions to address the dilemma between AI and the need for access to larger quantities of data for multiple purposes and privacy should be emphasized.

Discrimination Law

A growing area of research globally is the social consequences of AI with a particular focus on its tendency to replicate or amplify existing and structural inequalities. Problems such as data invisibility of certain excluded groups,^{^[50]} the myth of data objectivity and neutrality,^{^[51]} and data monopolization^{^[52]} contribute to the disparate impacts of big data and AI. So far much of the research on this subject has not moved beyond the exploratory phase as is reflected in the reports released by the White House^{^[53]} and Federal Trade Commission^{^[54]} in the United States. The biggest challenge in addressing discriminatory and disparate impacts of AI is ascertaining “where value-added personalization and segmentation ends and where harmful discrimination begins.”^{^[55]}

Some prominent cases where AI can have discriminatory impact are denial of loans based on attributes such as neighbourhood of residence as a proxies which can be used to circumvent anti-discrimination laws which prevent adverse determination on the grounds of race, religion, caste or gender, or adverse findings by predictive policing against persons who are unfavorably represented in the structurally biased datasets used by the law enforcement agencies. There is a dire need for disparate impact regulation in sectors which see the emerging use of AI.

Similar to disparate impact regulation, developments in AI, and its utilisation, especially in credit rating, or risk assessment processes could create complex problems that cannot be solved only by the principle based regulation. Instead, regulation intended specifically to avoid outcomes that the regulators feel are completely against the consumer, could be an additional tool that increases the fairness, and effectiveness of the system.

Competition Law

The conversation of use of competition or antitrust laws to govern AI is still at an early stage. However, the emergence of numerous data driven mergers or acquisitions such as Yahoo-Verizon, Microsoft-LinkedIn and Facebook-WhatsApp have made it difficult to ignore the potential role of competition law in the governance of data collection and processing practices. It is important to note that the impact of Big Data goes far beyond digital markets and the mergers of companies such as Bayer, Climate Corp and Monsanto shows that data driven business models can also lead to the convergence of companies from completely different sectors as well. So far, courts in Europe have looked at questions such as the impact of combination of databases on competition^{^[56]} and have held that in the context of merger control, data can be a relevant question if an undertaking achieves a dominant position through a merger, making it capable of gaining further market power through increased amounts of customer data. The evaluation of the market advantages of specific datasets has already been done in the past, and factors which have been deemed to be relevant have included whether the dataset could be replicated under reasonable conditions by competitors and whether the use of the dataset was likely to result in a significant competitive advantage.^{^[57]} However, there are limited circumstances in which big data meets the four traditional criteria for being a barrier to entry or a source of sustainable competitive advantage — inimitability, rarity, value, and non-substitutability.^{^[58]}

Any use of competition law to curb data-exclusionary or data-exploitative practices will first have to meet the threshold of establishing capacity for a firm to derive market power from its ability to sustain datasets unavailable to its competitors. In this context the peculiar ways in which network effects, multi-homing practices and how dynamic the digital markets are, are all relevant factors which could have both positive and negative impacts on competition. There is a need for greater discussion on data as a sources of market power in both digital and non-digital markets, and how this legal position can used to curb data monopolies, especially in light of government backed monopolies for identity verification and payments in India.

Consumer Protection Law

The Consumer Protection Bill, 2015, tabled in the Parliament towards the end of the monsoon session has introduced an expansive definition of the term “unfair trade practices.” The definition as per the Bill includes the disclosure “to any other person any personal information given in confidence by the consumer.” This clause excludes from the scope of unfair trade practices, disclosures under provisions of any law in force or in public interest. This provision could have significant impact on the personal data protection law in India. Alongside, there is also a need to ensure that principles such as safeguarding consumers personal information in order to ensure that the same is not used to their detriment are included within the definition of unfair trade practices. This would provide consumers an efficient and relatively speedy forum to contest adverse impacts on them of data driven decision-making.

Sectoral Regulation

Our research into sectoral case studies revealed that there are a number of existing sectoral laws and policies that are applicable to aspects of AI. For example, in the health sector there is the Medical Council Professional Conduct, Etiquette, and Ethics Regulations 2002, the Electronic Health Records Standards 2016, the draft Medical Devices Rules 2017, the draft Digital Information Security in Healthcare Act. In the finance sector there is the Credit Information Companies (Regulation) Act 2005 and 2006, the Securities and Exchange Board of India (Investment Advisers) Regulations, 2013, the Payment and Settlement Systems Act, 2007, the Banking Regulations Act 1949, SEBI guidelines on robo advisors etc. Before new regulations, guidelines etc are developed - a comprehensive exercise needs to be undertaken at a sectoral level to understand if 1. sectoral policy adequately addresses the changes being brought about by AI 2. If it does not - is an amendment possible and if not - what form of policy would fill the gap.

Principled approach

Transparency

Audits

Internal and external audits can be mechanisms towards creating transparency about the processes and results of AI solutions as they are implemented in a specific context. Audits can take place while a solution is still in ‘pilot’ mode and on a regular basis during implementation. For example, in the Payment Card Industry (PCI) tool, transparency is achieved through frequent audits, the results of which are simultaneously and instantly transmitted to the regulator and the developer. Ideally parts of the results of the audit are also made available to the public, even if the entire results are not shared.

Tiered Levels of Transparency

There are different levels and forms of transparency as well as different ways of achieving the same. The type and form of transparency can be tiered and dependent on factors such as criticality of function, potential direct and indirect harm, sensitivity of data involved, actor using the solution . The audience can also be tiered and could range from an individual user to senior level positions, to oversight bodies.

Human Facing Transparency

It will be important for India to define standards around human-machine interaction including the level of transparency that will be required. Will chatbots need to disclose that they are chatbots? Will a notice need to be posted that facial recognition technology is used in a CCTV camera? Will a company need to disclose in terms of service and privacy policies that data is processed via an AI driven solution? Will there be a distinction if the AI takes the decision autonomously vs. if the AI played an augmenting role? Presently, the Niti Aayog paper has been silent on this question.

Explainability

An explanation is not equivalent to complete transparency. The obligation of providing an explanation does not mean that the developer should necessarily know the flow of bits through the AI system. Instead, the legal requirement of providing an explanation requires an ability to explain how certain parameters may be utilised to arrive at an outcome in a certain situation.

Doshi-Velez and Kortz have highlighted two technical ideas that may enhance a developer's ability to explain the functioning of AI systems:^{^[59]}

1) Differentiation and processing: AI systems are designed to have the inputs differentiated and processed through various forms of computation-in a reproducible and robust manner. Therefore, developers should be able to explain a particular decision by examining the inputs in an attempt to determine which of them have the greatest impact on the outcome.

2) Counterfactual faithfulness: The second property of counterfactual faithfulness enables the developer to consider which factors caused a difference in the outcomes. Both these solutions can be deployed without necessarily knowing the contents of black boxes. As per Pasquale, ‘Explainability matters because the process of reason-giving is intrinsic to juridical determinations – not simply one modular characteristic jettisoned as anachronistic once automated prediction is sufficiently advanced.”^{^[60]}

Rules based system applied contextually

Oswald et al have suggested two proposals that might mitigate algorithmic opacity.by designing a broad rules-based system, whose implementation need to be applied in a context-specific manner which thoroughly evaluates the key enablers and challengers in each specific use case.^{^[61]}

Experimental proportionality was designed to enable the courts to make proportionality determinations of an algorithm at the experimental stage even before the impacts are fully realised in a manner that would enable them to ensure that appropriate metrics for performance evaluation and cohesive principles of design have been adopted. In such cases they recommend that the courts give the benefit of the doubt to the public sector body subject to another hearing within a stipulated period of time once data on the impacts of the algorithm become more readily available.
‘ALGO-CARE' calls for the design of a rules-based system which ensures that the algorithms^{^[62]} are:

(1) Advisory: Algorithms must retain an advisory capacity that augments existing human capability rather than replacing human discretion outright;

(2) Lawful: Algorithm's proposed function, application, individual effect and use of datasets should be considered in symbiosis with necessity, proportionality and data minimisation principles;

(3) Granularity: Issues such as data analysis issues such as meaning of data, challenges stemming from disparate tracts of data, omitted data and inferences should be key points in the implementation process;

(4) Ownership: Due regard should be given to intellectual property ownership but in the case of algorithms used for governance, it may be better to have open source algorithms at the default. Regardless of the sector,the developer must ensure that the algorithm works in a manner that enables a third party to investigate the workings of the algorithm in an adversarial judicial context.

(5)Challengeable:The results of algorithmic analysis should be applied with regard to professional codes and regulations and be challengeable. In a report evaluating the NITI AAYOG Discussion Paper, CIS has argued that AI that is used for governance , must be made auditable in the public domain,if not under Free and Open Source Software (FOSS)-particularly in the case of AI that has implications for fundamental rights.^{^[63]}

(6) Accuracy: The design of the algorithm should check for accuracy;

(7) Responsible: Should consider a wider set of ethical and moral principles and the foundations of human rights as a guarantor of human dignity at all levels and

(8) Explainable: Machine Learning should be interpretable and accountable.

A rules based system like ALGO-CARE can enable predictability in use frameworks for AI. Predictability compliments and strengthens transparency.

Accountability

Conduct Impact Assessment

There is a need to evolve Algorithmic Impact Assessment frameworks for the different sectors in India, which should address issues of bias, unfairness and other harmful impacts of use of automated decision making. AI is a nascent field and the impact of the technology on the economy, society, etc. is still yet to be fully understood. Impact assessment standards will be important in identifying and addressing potential or existing harms and could potentially be more important in sectors or uses where there is direct human interaction with AI or power dimensions - such as in healthcare or use by the government. A 2018 Report by the AI Now Institute lists methods that should be adopted by the government for conducting his holistic assessment^{^[64]}: These should include: (1) Self-assessment by the government department in charge of implementing the technology, (2)Development of meaningful inter-disciplinary external researcher review mechanisms, (3) Notice to the public regarding self-assessment and external review, (4)Soliciting of public comments for clarification or concerns, (5) Special regard to vulnerable communities who may not be able to exercise their voice in public proceedings. An adequate review mechanism which holistically evaluates the impact of AI would ideally include all five of these components in conjunction with each other.

Regulation of Algorithms

Experts have voiced concerns about AI mimicking human prejudices due to the biases present in the Machine Learning algorithms. Scientists have revealed through their research that machine learning algorithms can imbibe gender and racial prejudices which are ingrained in language patterns or data collection processes. Since AI and machine algorithms are data driven, they arrive at results and solutions based on available
and historical data. When this data itself is biased, the solutions presented by the AI will also be biased. While this is inherently discriminatory, scientists have provided solutions to rectify these biases which can occur at various stages by introducing a counter bias at another stage. It has also been suggested that data samples should be shaped in such a manner so as to minimise the chances of algorithmic bias. Ideally regulation of algorithms could be tailored - explainability, traceability, scrutability. We recommend that the national strategy on AI policy must take these factors into account and combination of a central agency driving the agenda, and sectoral actors framing regulations around specific uses of AI that are problematic and implementation is required.

As the government begins to adopt AI into governance - the extent to which and the circumstances autonomous decision making capabilities can be delegated to AI need to be questioned. Questions on whether AI should be autonomous, should always have a human in the loop, and should have a ‘kill-switch’ when used in such contexts also need to be answered. A framework or high level principles can help to guide these determinations. For example:

Modeling Human Behaviour: An AI solution trying to model human behaviour, as in the case of judicial decision-making or predictive policing may need to be more regulated, adhere to stricter standards, and need more oversight than an algorithm that is trying to predict ‘natural’ phenomenon such as traffic congestion or weather patterns.
Human Impact: An AI solution which could cause greater harm if applied erroneously-such as a robot soldier that mistakenly targets a civilian requires a different level and framework of regulation than an AI solution designed to create a learning path for a student in the education sector and errs in making an appropriate assessment..
Primary User: AI solutions whose primary users are state agents attempting to discharge duties in the public interest such as policemen, should be approached with more caution than those used by individuals such as farmers getting weather alerts

Fairness

It is possible to incorporate broad definitions of fairness into a wide range of data analysis and classification systems.^{^[65]} While there can be no bright-line rules that will necessarily enable the operator or designer of a Machine Learning System to arrive at an ex ante determination of fairness, from a public policy perspective, there must be a set of rules or best practices that explain how notions of fairness should be utilised in the real world applications of AI-driven solutions.^{^[66]} While broad parameters should be encoded by the developer to ensure compliance with constitutional standards, it is also crucial that the functioning of the algorithm allows for an ex-post determination of fairness by an independent oversight body if the impact of the AI driven solution is challenged.

Further, while there is no precedent on this anywhere in the world, India could consider establishing a Committee entrusted with the specific task of continuously evaluating the operation of AI-driven algorithms. Questions that the government would need to answer with regard to this body include:

What should the composition of the body be?
What should be the procedural mechanisms that govern the operation of the body?
When should the review committee step in? This is crucial because excessive review may re-entrench the bureaucracy that the AI driven solution was looking to eliminate.
What information will be necessary for the review committee to carry out its determination? Will there be conflicts with IP, and if so how will these be resolved?
To what degree will the findings of the committee be made public?
What powers will the committee have? Beyond making determinations, how will these be enforced?

Market incentives

Standards as a means to address data issues

With digitisation of legacy records and the ability to capture more granular data digitally, one of the biggest challenges facing Big Data is a lack of standardised data and interoperability frameworks. This is particularly true in the healthcare and medicine sector where medical records do not follow a clear standard, which poses a challenge to their datafication and analysis. The presence of developed standards in data management and exchange, interoperable Distributed Application Platform and Services, Semantic related standards for markup, structure, query, semantics, Information access and exchange have been spoken of as essential to address the issues of lack of standards in Big Data.^{^[67]}

Towards enabling usability of data, it is important that clear data standards are established. This has been recognized by Niti Aayog in its National Strategy for AI. On one hand, there can operational issues with allowing each organisation to choose their own specific standards to operate under, while on the other hand, non-uniform digitisation of data will also cause several practical problems, most primarily to do with interoperability of the individual services, as well as their usability. For instance, in the healthcare sector, though India has adopted an EHR policy, implementation of this policy is not yet harmonized - leading to different interpretations of ‘digitizing records (i.e taking snapshots of doctor notes), retention methods and periods, and comprehensive implementation across all hospital data. Similarly, while independent banks and other financial organisations are already following, or in the process of developing internal practices,there exist no uniform standards for digitisation of financial data. As AI development, and application becomes more mainstream in the financial sector, the lack of a fixed standard could create significant problems.

Better Design Principles in Data Collection

An enduring criticism of the existing notice and consent framework has been that long, verbose and unintelligible privacy notices are not efficient in informing individuals and helping them make rational choices. While this problem predates Big Data, it has only become more pronounced in recent times, given the ubiquity of data collection and implicit ways in which data is being collected and harvested. Further, constrained interfaces on mobile devices, wearables, and smart home devices connected in an Internet of Things amplify the usability issues of the privacy notices. Some of the issues with privacy notices include Notice complexity, lack of real choices, notices decoupled from the system collecting data etc. An industry standard for a design approach to privacy notices which includes looking at factors such as the timing of the notice, the channels used for communicating the notices, the modality (written, audio, machine readable, visual) of the notice and whether the notice only provides information or also include choices within its framework, would be of great help. Further, use of privacy by design principles can be done not just at the level of privacy notices but at each step of the information flow, and the architecture of the system can be geared towards more privacy enhanced choices.

^{^[1]} https://cis-india.org/internet-governance/blog/artificial-intelligence-in-india-a-compendium

^{^[2]} https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

^{^[3]} https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf

^{^[4]} https://www.gov.uk/government/publications/artificial-intelligence-sector-deal/ai-sector-deal

^{^[5]} http://www.nedo.go.jp/content/100865202.pdf

^{^[6]} https://www.eu-robotics.net/sparc/10-success-stories/european-robotics-creating-new-markets.html?changelang=2

^{^[7]} https://www.cifar.ca/ai/pan-canadian-artificial-intelligence-strategy

^{^[8]} https://www.newamerica.org/cybersecurity-initiative/blog/chinas-plan-lead-ai-purpose-prospects-and-problems/

^{^[9]} http://www.uaeai.ae/en/

^{^[10]} https://www.aisingapore.org/

^{^[11]} https://news.joins.com/article/22625271

^{^[12]} https://www.aiforhumanity.fr/pdfs/MissionVillani_Report_ENG-VF.pdf

^{^[13]} https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe https://www.euractiv.com/section/digital/news/twenty-four-eu-countries-sign-artificial-intelligence-pact-in-bid-to-compete-with-us-china/

^{^[14]} https://www.aitf.org.in/

^{^[15]} http://www.niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf

^{^[16]} https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

^{^[17]} https://www.cifar.ca/ai/pan-canadian-artificial-intelligence-strategy

^{^[18]} https://cis-india.org/internet-governance/blog/the-ai-task-force-report-the-first-steps-towards-indias-ai-framework

^{^[19]} https://cis-india.org/internet-governance/blog/niti-aayog-discussion-paper-an-aspirational-step-towards-india2019s-ai-policy

^{^[20]} https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe

^{^[21]} http://pib.nic.in/newsite/PrintRelease.aspx?relid=181007

^{^[22]} Ryan Calo, 2017 Artificial Intelligence Policy: A Primer and Roadmap. U.C. Davis L. Review,

Vol. 51, pp. 398 - 435.

^{^[23]} https://trai.gov.in/sites/default/files/CIS_07_11_2017.pdf

^{^[24]} https://www.datatilsynet.no/globalassets/global/english/ai-and-privacy.pdf

^{^[25]} http://www.niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf

^{^[26]} https://martechtoday.com/bottos-launches-a-marketplace-for-data-to-train-ai-models-214265

^{^[27]} https://opensource.com/article/18/5/top-8-open-source-ai-technologies-machine-learning

^{^[28]} Amanda Levendowski, How Copyright Law Can Fix Artificial Intelligence’s

Implicit Bias Problem, 93 WASH. L. REV. (forthcoming 2018) (manuscript at 23, 27-32),

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3024938.

^{^[29]} Id.

^{^[30]} H. Brendan McMahan, et al., Communication-Efficient Learning of Deep Networks

from Decentralized Data, arXiv:1602.05629 (Feb. 17, 2016), https://arxiv.org/abs/1602.05629.

^{^[31]} Id.

^{^[32]} Pierre N. Leval, Nimmer Lecture: Fair Use Rescued, 44 UCLA L. REV. 1449, 1457 (1997).

^{^[33]} https://cis-india.org/internet-governance/blog/niti-aayog-discussion-paper-an-aspirational-step-towards-india2019s-ai-policy

^{^[34]} https://cis-india.org/internet-governance/blog/niti-aayog-discussion-paper-an-aspirational-step-towards-india2019s-ai-policy

^{^[35]} Discussion Paper on National Strategy for Artificial Intelligence | NITI Aayog | National Institution for Transforming India. (n.d.) p. 54. Retrieved from http://niti.gov.in/content/national-strategy-ai-discussion-paper.

^{^[36]} Leverhulme Centre for the Future of Intelligence, http://lcfi.ac.uk/.

^{^[37]} AI Now, https://ainowinstitute.org/.

^{^[38]} https://cis-india.org/internet-governance/ai-and-governance-case-study-pdf

^{^[39]} http://iridescentlearning.org/

^{^[40]} https://cis-india.org/internet-governance/ai-and-governance-case-study-pdf

^{^[41]} Points, L., & Potton, E. (2017). Artificial intelligence and automation in the UK.

^{^[42]} Paul, Y., Hickok, E., Sinha, A. and Tiwari, U., Artificial Intelligence in the Healthcare Industry in India, Centre for Internet and Society. Available at https://cis-india.org/internet-governance/files/ai-and-healtchare-report.

^{^[43]} Goudarzi, S., Hickok, E., and Sinha, A., AI in the Banking and Finance Industry in India, Centre for Internet and Society. Available at https://cis-india.org/internet-governance/blog/ai-in-banking-and-finance.

^{^[44]} Paul, Y., Hickok, E., Sinha, A. and Tiwari, U., Artificial Intelligence in the Healthcare Industry in India, Centre for Internet and Society. Available at https://cis-india.org/internet-governance/files/ai-and-healtchare-report.

^{^[45]} https://news.microsoft.com/en-in/government-karnataka-inks-mou-microsoft-use-ai-digital-agriculture/

^{^[46]} https://news.microsoft.com/en-in/government-telangana-adopts-microsoft-cloud-becomes-first-state-use-artificial-intelligence-eye-care-screening-children/

^{^[47]} NITI Aayog. (2018). Discussion Paper on National Strategy for Artificial Intelligence. Retrieved from http://niti.gov.in/content/national-strategy-ai-discussion-paper. 18

^{^[48]} https://edps.europa.eu/sites/edp/files/publication/16-10-19_marrakesh_ai_paper_en.pdf

^{^[49]} https://cis-india.org/internet-governance/blog/the-srikrishna-committee-data-protection-bill-and-artificial-intelligence-in-india

^{^[50]} J. Schradie, The Digital Production Gap: The Digital Divide and Web 2.0 Collide. Elsevier Poetics, 39 (1).

^{^[51]} D Lazer, et al., The Parable of Google Flu: Traps in Big Data Analysis. Science. 343 (1).

^{^[52]} Danah Boyd and Kate Crawford, Critical Questions for Big Data. Information, Communication & Society. 15 (5).

^{^[53]} John Podesta, (2014) Big Data: Seizing Opportunities, Preserving Values, available at

http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf

^{^[54]} E. Ramirez, (2014) FTC to Examine Effects of Big Data on Low Income and Underserved Consumers at September Workshop, available at http://www.ftc.gov/news-events/press-releases/2014/04/ftc-examine-effects-big-data-lowincome-underserved-consumers

^{^[55]} M. Schrage, Big Data’s Dangerous New Era of Discrimination, available at http://blogs.hbr.org/2014/01/bigdatas-dangerous-new-era-of-discrimination/.

^{^[56]} Google/DoubleClick Merger case

^{^[57]} French Competition Authority, Opinion n°10-A-13 of 1406.2010,

http://www.autoritedelaconcurrence.fr/pdf/avis/10a13.pdf. That opinion of the Authority aimed at

giving general guidance on that subject. It did not focus on any particular market or industry

although it described a possible application of its analysis to the telecom industry.

^{^[58]} http://www.analysisgroup.com/is-big-data-a-true-source-of-market-power/#sthash.5ZHmrD1m.dpuf

^{^[59]} Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S., O'Brien, D., ... & Wood, A. (2017). Accountability of AI under the law: The role of explanation. arXiv preprint arXiv:1711.01134.

^{^[60]} Frank A. Pasquale ‘Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society’ (July 14, 2017). Ohio State Law Journal, Vol. 78, 2017; U of Maryland Legal Studies Research Paper No. 2017-21, 7.

^{^[61]} Oswald, M., Grace, J., Urwin, S., & Barnes, G. C. (2018). Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality. Information & Communications Technology Law, 27(2), 223-250.

^{^[62]} Ibid.

^{^[63]} Abraham S., Hickok E., Sinha A., Barooah S., Mohandas S., Bidare P. M., Dasgupta S., Ramachandran V., and Kumar S., NITI Aayog Discussion Paper: An aspirational step towards India’s AI policy. Retrieved from https://cis-india.org/internet-governance/files/niti-aayog-discussion-paper.

^{^[64]} Reisman D., Schultz J., Crawford K., Whittaker M., (2018, April) Algorithmic Impact Assessments: A Practical Framework For Public Agency Accountability. Retrieved from https://ainowinstitute.org/aiareport2018.pdf.

^{^[65]} Sample I., (2017, November 5) Computer says no: why making AIs fair, accountable and transparent is crucial. Retrieved from https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial.

^{^[66]} Kroll, J. A., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2016). Accountable algorithms. U. Pa. L. Rev., 165, 633.

^{^[67]} http://www.iso.org/iso/big_data_report-jtc1.pdf

For more details visit https://cis-india.org/internet-governance/blog/ai-in-india-a-policy-agenda

Meeting of Information Systems Security and Biometrics Sectional Committee

Admin — 2018-09-19T14:08:23Z

Gurshabad Grover attended the 14th meeting of the Information Systems Security and Biometrics Sectional Committee (LITD 17) of the Bureau of Indian Standards (BIS), which was held at the BIS office in New Delhi on 14 September 2018.

This was Gurshabad's first LITD 17 meeting. The committee noted my co-option in the committee and registration in Working Group 1 (Information security management systems) and WG5 (Identity management and privacy technologies) of ISO JTC 1 / SC 27 / “IT Security Techniques”. Some of the items discussed included proposed standards for biometric information protection, mobile phone security, and data privacy engineering & management practices.

For more details visit https://cis-india.org/internet-governance/news/meeting-of-information-systems-security-and-biometrics-sectional-committee

UNESCAP Google AI Meeting

Admin — 2018-09-20T15:47:42Z

Arindrajit was a panelist at the event on AI in public service delivery hosted by UNESCAP Bangkok on August 29, 2018. The event was co-organized by Economic and Social Commission for Asia and the Pacific and Google.

The discussion centered around the two questions (1) Is AI different from other technological advancements in the past and (2) Recommendations for policy-makers to enhance AI in Public Service Delivery.The other panelists were Dr. Urs Gasser (Berkman), Vidushi Marda ( Art.19), Malavika Jayaram (Digital Asia Hub) and Jake Lucchi ( Google) The panel was a platform to discuss some of our findings in our case studies on healthcare and agriculture, which we will receive comments on and will get published in November.

For more details visit https://cis-india.org/internet-governance/news/unescap-google-ai-meeting

Takshashila's online Cogitatum on AI and Ethics in India

Admin — 2018-09-26T01:46:39Z

Elonnai Hickok participated in an event organized by Takhshashila on August 27, 2018 and made a presentation on Ethics and AI in India. The event was held in Takshashila Institution

Click to view the slides

For more details visit https://cis-india.org/internet-governance/news/takshashilas-online-cogitatum-on-ai-and-ethics-in-india

Cross-Border Data Sharing and India: A study in Processes, Content and Capacity

Amber Sinha, Elonnai Hickok, Udbhav Tiwari and Arindrajit Basu — 2018-09-29T00:37:39Z

A majority of criminal investigations in the modern era necessitate law enforcement access to electronic evidence stored extra-territorially. The conventional methods of compelling the presentation of evidence available for investigative agencies often fail when the evidence is not present within the territorial boundaries of the state.

The crux of the issue lies in the age old international law tenet of territorial sovereignty.Investigating crimes is a sovereign act and it cannot be exercised in the territory of another country without that country’s consent or through a permissive principle of extra-territorial jurisdiction. Certain countries have explicit statutory provisions which disallow companies incorporated in their territory from disclosing data to foreign jurisdictions. The United States of America, which houses most of the leading technological firms like Google, Apple, Microsoft, Facebook, and Whatsapp, has this requirement.

This necessitates a consent based international model for cross border data sharing as a completely ad-hoc system of requests for each investigation would be ineffective. Towards this, Mutual Legal Assistance Treaties (MLATs) are the most widely used method for cross border data sharing, with letters rogatory, emergency requests and informal requests being other methods available to most investigators. While recent gambits towards ring-fencing the data within Indian shores might alter the contours of the debate, a sustainable long-term strategy requires a coherent negotiation strategy that enables co-operation with a range of international partners.

This negotiation strategy needs to be underscored by domestic safeguards that ensure human rights guarantees in compliance with international standards, robust identification and augmentation of capacity and clear articulation of how India’s strategy lines up with the existing tenets of International law. This report studies the workings of the Mutual Legal Assistance Treaty (MLAT) between the USA and India and identifies hurdles in its existing form, culls out suggestions for improvement and explores how recent legislative developments, such as the CLOUD Act might alter the landscape.

The path forward lies in undertaking process based reforms within India with an eye on leveraging these developments to articulate a strategically beneficial when negotiating with external partners.As the nature of policing changes to a model that increasingly relies on electronic evidence, India needs to ensure that it’s technical strides made in accessing this evidence is not held back by the lack of an enabling policy environment. While the data localisation provisions introduced in the draft Personal Data Protection Bill may alter the landscape once it becomes law, this paper retains its relevance in terms of guiding the processes, content and capacity to adequately manoeuvre the present conflict of laws situation and accessing data not belonging to Indians that may be needed for criminal investigations.As a disclaimer,the report and graphics contained within it have been drafted using publicly available information and may not reflect real world practices.

Click here to download the report With research assistance from Sarath Mathew and Navya Alam and visualisation by Saumyaa Naidu

For more details visit https://cis-india.org/internet-governance/blog/cross-border-data-sharing-and-india-a-study-in-processes-content-and-capacity

After Supreme Court Setback, Fintech Firms Await Clarity On Aadhaar

Admin — 2018-10-01T23:39:42Z

The 12-digit Aadhaar number is now out of bounds for fintech companies in India.

The article by Nishant Sharma was published in Bloomberg Quint on September 27, 2018. Pranesh Prakash was quoted.

Video

With the Supreme Court on Wednesday terming Aadhaar authentication by private companies as “unconstitutional”, companies such as online wallets and e-tailers, among others, will now have to make changes to how they onboard and verify customers, in addition to how they transact.

In a 567-page majority judgment authored by Justice Sikri and concurred upon by two other judges—Chief Justice Dipak Misra and Justice AM Khanwilkar—it said that Section 57 of the Aadhaar Act, which allows private companies to use Aadhaar for authentication services based on a contract between the corporate and an individual, would enable commercial exploitation of private data and hence is unconstitutional.

“What it essentially means is that the private bodies, such as lending platforms, wallets, or any private entity, cannot use Aadhaar for authentication,” said Anirudh Rastogi founder at Ikigai Law (formerly TRA), a law firm that specialises in representing businesses on data privacy.

The decision is set to impact private companies right from Flipkart-owned PhonePe, Paytm, Reliance Jio and Amazon, among others, which rely on Aadhaar for e-verification. Amazon recently launched cardless equated monthly installments on Amazon Pay through the digital finance platform Capital Float and asked customers to provide Aadhaar numbers or virtual ID and PAN details on the Amazon app for verification.

'Aadhaar Is Just Another ID'

Pranesh Prakash, fellow, Centre for Internet and Society, said that with this judgment Aadhaar is no longer an identity infrastructure as its creators have dreamt of. “It is now just another ID.”

For those opposed to Aadhaar, on privacy and security grounds, this may be a part victory. But for the Fintech industry it stymies the use of quick Aadhaar-based e-KYC (know your customer norms) to onboard customers. “The fintech industry thrives on the instant paperless mantra, and this move will curb its rapid growth, ” Amrish Rau, co-founder of PayU, said in a text message.

The verdict is also set to push up costs for the industry. Rau said: “Conducting physical KYC would be a costly affair, with every physical KYC costing about Rs 100 per person.”

Companies like PhonePe await more clarity. “We are waiting to hear from bodies like the Reserve Bank of India, UIDAI on what KYC that will be required for wallets moving ahead," Sameer Nigam, cofounder of PhonePe, said. "Whether we go to no KYC, lower limit environment or go to the physical KYC environment."

The judgment also stated that the identification number will not be mandatory for opening bank accounts, mobile-phone connections or for admissions into educational institutions. However, Aadhaar will continue to be mandatory for the distribution of state-sponsored welfare schemes including direct benefit transfers and the public distribution system. Taxpayers will have to link their Permanent Account Numbers to the biometric database.

Aadhaar-Based KYC: Allowed With Consent?

The Supreme Court has concluded that the part of section 57 which enables body corporate and individuals also to seek authentication, that too on the basis of a contract between the individual and such body corporate or person, would impinge upon the right to privacy of such individuals.

Prasanna S, a Supreme Court advocate and lawyer for one of the petitioners in the Aadhaar matter interpreted it to mean that even if a customer voluntarily wants to use Aadhaar for e-KYC, businesses cannot accept it.

They have struck down the part of Section 57 that allows use of Aadhaar based on a contract. A contract, by nature is voluntary, But since the court has struck down this part, even voluntary use won’t be permitted.

Prasanna S, Advocate, Supreme Court

Jaitley Hints At Legal Backing

Meanwhile, Finance Minister Arun Jaitley on Wednesday hinted that the Centre is likely to examine whether separate legal backing is needed for Section 57 of the Aadhaar Act, the newswire PTI reported. “So, let us first read the judgement. There are two-three prohibited areas. Are they because they are totally prohibited or are they because they need legal backing,” Jaitley was quoted as saying.

Rastogi of Ikigai Law said that the court has left open for the government to promulgate a law to enable private parties to use Aadhaar that can withstand judicial scrutiny.

Rahul Matthan, a technology partner at law firm Trilegal differed with this view. He said that since the apex court has ruled that private entities cannot access the Aadhaar infrastructure, it means that even if the government brings a specific law to allow for that, it would be unconstitutional.

Prasanna agreed with this interpretation.

The court has hinted that commercial exploitation of personal information will fail the proportionality test laid down by it in the Right to Privacy judgment. This is one of the grounds for them to conclude that Section 57 is unconstitutional. So even a law is introduced, private access will be impermissible.

Prasanna S, Advocate, Supreme Court

Are Aadhaar-Based KYCs Tainted?

Since the use of Aadhaar by private entities has been struck down, does it mean entities who have used it for KYC so far have to re-do that exercise? And data that was collected as part of Aadhaar-based KYC- does that need to be deleted?

The majority order hasn’t specifically addressed these questions, Matthan pointed out. But went on to explain that his reading of the judgment is that the court wants things to remain as they are.

The Supreme Court has said that collection of data before the Aadhaar Act was introduced is valid. If you follow that sentiment, may be we can argue that there’s no requirement to delete the data.

Rahul Matthan, Partner, Trilegal

Whatever has been done without the authority of law has to go, Prasanna said. But this outcome may not be practical and another hearing before the Supreme Court may be required to clear these questions, he added.

Private entities such as the online cab aggregator Ola have already removed eKYC from its e-wallet when BloombergQuint last checked. Others may follow suit.

For more details visit https://cis-india.org/internet-governance/news/bloomberg-quint-nishant-sharma-september-27-2018-after-sc-setback-fintech-firms-await-clarity-on-aadhaar

Community Standards Roundtable Conversations

Admin — 2018-10-16T14:01:55Z

Ambika Tandon was a participant in a roundtable organized by Facebook, School of Media & Cultural Studies, and Tata Institute of Social Sciences in Bengaluru on October 7, 2018.

The agenda for the roundtable was to discuss their community standards, particularly hate speech and harassment, and receive feedback from a feminist and gendered lens. Click to read more.

For more details visit https://cis-india.org/internet-governance/news/community-standards-roundtable-conversations

Why Data Localisation Might Lead To Unchecked Surveillance

pranesh — 2018-10-16T14:08:34Z

In recent times, there has been a rash of policies and regulations that propose that the data that Indian entities handle be physically stored on servers in India, in some cases exclusively. In other cases, only a copy needs to be stored.

The article was published in Bloomberg Quint on October 15, 2018 and also mirrored in the Quint.

In April 2018, the Reserve Bank of India put out a circular requiring that all “data relating to payment systems operated by them are stored in a system only in India” within six months. Lesser requirements have been imposed on all Indian companies’ accounting data since 2014 (the back-up of the books of account and other books that are stored electronically must be stored in India, the broadcasting sector under the Foreign Direct Investment policy, must locally store subscriber information, and the telecom sector under the Unified Access licence, may not transfer their subscriber data outside India).

The draft e-commerce policy has a wide-ranging requirement of exclusive local storage for “community data collected by Internet of Things devices in public space” and “data generated by users in India from various sources including e-commerce platforms, social media, search engines, etc.”, as does the draft e-pharmacy regulations, which stipulate that “the data generated” by e-pharmacy portals be stored only locally.

While companies such as Airtel, Reliance, PhonePe (majority-owned by Walmart) and Alibaba, have spoken up in support the government’s data localisation efforts, others like Facebook, Amazon, Microsoft, and Mastercard have led the way in opposing it.

Just this week, two U.S. Senators wrote to the Prime Minister’s office arguing that the RBI’s data localisation regulations along with the proposals in the draft e-commerce and cloud computing policies are “key trade barriers”. In her dissenting note to the Srikrishna Committee's report, Rama Vedashree of the Data Security Council of India notes that, “mandating localisation may potentially become a trade barrier and the key markets for the industry could mandate similar barriers on data flow to India, which could disrupt the IT-BPM (information technology-business process management) industry.”

Justification For Data Localisation

What are the reasons for these moves towards data localisation?

Given the opacity of policymaking in India, many of the policies and regulations provide no justification at all. Even the ones that do, don’t provide cogent reasoning.

The RBI says it needs “unfettered supervisory access” and hence needs data to be stored in India. However, it fails to state why such unfettered access is not possible for data stored outside of India.

As long as an entity can be compelled by Indian laws to engage in local data storage, that same entity can also be compelled by that same law to provide access to their non-local data, which would be just as effective.

What if they don’t provide such access? Would they be blacklisted from operating in India, just as they would if they didn’t engage in local data storage? Is there any investigatory benefit to storing data in India? As any data forensic expert would note, chain of custody and data integrity are what are most important components of data handling in fraud investigation, and not physical access to hard drives. It would be difficult for the government to say that it will block all Google services if the company doesn’t provide all the data that Indian law enforcement agencies request from it. However, it would be facile for the RBI to bar Google Pay from operating in India if Google doesn’t provide it “unfettered supervisory access” to data.

The most exhaustive justification of data localisation in any official Indian policy document is that contained in the Srikrishna Committee’s report on data protection. The report argues that there are several benefits to data localisation:

Effective enforcement,
Avoiding reliance on undersea cables,
Avoiding foreign surveillance on data stored outside India,
Building an “Artificial Intelligence ecosystem”

Of these, the last three reasons are risible.

Not A Barrier To Surveillance

Requiring mirroring of personal data on Indian servers will not magically give rise to experts skilled in statistics, machine learning, or artificial intelligence, nor will it somehow lead to the development of the infrastructure needed for AI.

The United States and China are both global leaders in AI, yet no one would argue that China’s data localisation policies have helped it or that America’s lack of data localisation polices have hampered it.

On the question of foreign surveillance, data mirroring will not have any impact, since the Srikrishna Committee’s recommendation would not prevent companies from storing most personal data outside of India.

Even for “sensitive personal data” and for “critical personal data”, which may be required to be stored in India alone, such measures are unlikely to prevent agencies like the U.S. National Security Agency or the United Kingdom’s Government Communications Headquarters from being able to indulge in extraterritorial surveillance.

In 2013, slides from an NSA presentation that were leaked by Edward Snowden showed that the NSA’s “BOUNDLESSINFORMANT” programme collected 12.6 billion instances of telephony and Internet metadata (for instance, which websites you visited and who all you called) from India in just one month, making India one of the top 5 targets.

This shows that technically, surveillance in India is not a challenge for the NSA.

So, forcing data mirroring enhances Indian domestic intelligence agencies’ abilities to engage in surveillance, without doing much to diminish the abilities of skilled foreign intelligence agencies.

As I have noted in the past, the technological solution to reducing mass surveillance is to use decentralised and federated services with built-in encryption, using open standards and open source software.

Reducing reliance on undersea cables is, just like reducing foreign surveillance on Indians’ data, a laudable goal. However, a mandate of mirroring personal data in India, which is what the draft Data Protection Bill proposes for all non-sensitive personal data, will not help. Data will stay within India if the processing happens within India. However, if the processing happens outside of India, as is often the case, then undersea cables will still need to be relied upon.

The better way to keep data within India is to incentivise the creation of data centres and working towards reducing the cost of internet interconnection by encouraging more peering among Internet connectivity providers.

While data mirroring will not help in improving the enforcement of any data protection or privacy law, it will aid Indian law enforcement agencies in gaining easier access to personal data.

The MLAT Route

Currently, many forms of law enforcement agency requests for data have to go through onerous channels called ‘mutual legal assistance treaties’. These MLAT requests take time and are ill-suited to the needs of modern criminal investigations. However, the U.S., recognising this, passed a law called the CLOUD Act in March 2018. While the CLOUD Act compels companies like Google and Amazon, which have data stored in Indian data centres, to provide that data upon receiving legal requests from U.S. law enforcement agencies, it also enables easier access to foreign law enforcement agencies to data stored in the U.S. as long as they fulfill certain procedural and rule-of-law checks.

While the Srikrishna Committee does acknowledge the CLOUD Act in a footnote, it doesn’t analyse its impact, doesn’t provide suggestions on how India can do this, and only outlines the negative consequences of MLATs.

Further, it is inconceivable that the millions of foreign services that Indians access and provide their personal data to will suddenly find a data centre in India and will start keeping such personal data in India. Instead, a much likelier outcome, one which the Srikrishna Committee doesn’t even examine, is that many smaller web services may find such requirements too onerous and opt to block users from India, similar to the way that Indiatimes and the Los Angeles Times opted to block all readers from the European Union due to the coming into force of the new data protection law.

The government could be spending its political will on finding solutions to the law enforcement agency data access question, and negotiating solutions at the international level, especially with the U.S. government. However it is not doing so.

Given this, the recent spate of data localisation policies and regulation can only be seen as part of an attempt to increase the scope and ease of the Indian government’s surveillance activities, while India’s privacy laws still remain very weak and offer inadequate legal protection against privacy-violating surveillance. Because of this, we should be wary of such requirements, as well as of the companies that are vocal in embracing data localisation.

For more details visit https://cis-india.org/internet-governance/blog/bloomberg-quint-pranesh-prakash-october-15-2018-why-data-localisation-might-lead-to-unchecked-surveillance

Sales of surveillance cameras are soaring, raising questions about privacy

Admin — 2018-10-16T14:22:55Z

The Telangana government wants more eyes on the streets to upgrade Hyderabad’s safety. It has asked enterprises, public sectors, residential associations and individuals to install closed-circuit television cameras (CCTVs) in and around their premises.

The article by Rahul Sachitanand was published in Economic Times on October 14, 2018. Elonnai Hickok was quoted.

More than a lakh CCTVs are expected to be installed across the city in the next few years. The initiative is part of the Nenu Saitham (Telugu for Me Too) project — being promoted by Hyderabad Police, which will monitor the feed. To ensure that lowquality CCTVs are not installed and the project is sustainable, the police has asked citizens to only buy from selected vendors.

With this move, launched in November 2017, the Telangana govt joins a growing list of governments, corporations, educational institutes, residential buildings and small businesses across the country that are buying such technology.

According to industry estimates, over a million surveillance units were sold every month a couple of years ago. Now it is two million. The Indian market is growing 20-25% annually, say experts. Frost & Sullivan says the security & surveillance market was worth Rs 8,200 crore in FY2017, reached Rs 11,000 crore in FY2018 and is expected to touch Rs 20,000 crore in FY2020.

The rise in CCTV coverage can also be observed anecdotally. There’s a steady uptick in CCTV clips circulating on Whatsapp, capturing crimes or funny events that would otherwise have gone undocumented. Many of the sensational crimes recently, including multiple incidents of murder in Tamil Nadu, were captured on CCTV cameras, distilling the pure horror of those moments on our mobile screens, and also offering valuable proof to nail the culprits.

The surveillance and security boom is fed by several companies, ranging from homegrown firms such as CP Plus to joint ventures such as Prama Hikvision to multinationals such as Bosch, Panasonic, Honeywell and Axis. The Telangana project, for example, helped Sweden-based Axis Communications widen its India market. It has already installed 1,500 cameras, and more will be installed soon. Other state governments have or are in the process of placing orders.

The Swedish company says it recently installed cameras and associated technology across a range of large corporate and government establishments across India. “We are at the beginning of a five-year boom cycle for these devices,” says Sudhindra Holla, sales director (India & Saarc), Axis Communications. “We are catering to a rush of orders ranging from large companies with complex security infrastructure to deals from government agencies in small towns such as Nanded and Kolhapur.”

Multiple factors are driving the growth in the CCTV segment, says Manu Tiwari, programme manager (automation and electronics practice), Frost and Sullivan. A strong government push to enhance security; purchases for initiatives such as the Smart City project, which covers 100 cities, and the Rs 2,219 crore allocated under the Nirbhaya Fund for women’s safety, which covers eight cities, are some of the growth drivers.

According to Sanjay Kaushik, managing director of security consultancy Netrika Consulting, there is a push to better use CCTV feeds to improve security across India. “While the focus hitherto has been on post facto scouting of footage to find perpetrators, organisations are now trying to be more proactive with their monitoring to spot suspicious people and packages before crimes occur.”

This could involve closely looking at footage to spot suspicious movements at places such as malls or airports or using technology to spot suspicious objects left unattended for long periods. Then, there’s also a focus on making sure the cameras are installed correctly. “Recognisability is key. Organisations are being pushed to ensure simple things like camera feeds are free of obstructions, licence plates are visible in feeds and there is adequate lighting,” adds Kaushik. Advances in technology have ensured that CCTV systems are cheaper and more accessible.

While large enterprises had taken to such technology earlier, even smaller commercial establishments and private residents now can afford to install security systems. The prices have practically halved over the last couple of years. An entry-level camera is now available for a little over Rs 2,000. “Even the cost of an integrated solution, which was as much as Rs 40,000 to Rs 50,000 three or four years ago, is today available for as little as Rs 15,000,” says Yogesh Dutta, COO of New Delhi-based CP Plus. “A rapid increase in the number of CCTVs sellers and technicians has also helped widen access.” The devices have become popular as it helps law enforcers to tackle crime, he adds.

CP Plus’s customers include Vedanta Power and Odisha Police, which has also decided to use e-surveillance to enhance security. Frost and Sullivan says small & medium enterprises and large corporations were together the biggest end-user segments in FY18. This segment had a market share of 33%. Residential had a 28% market share; the industrial segment had 18% and the government 13%, it said. Other major end-user segments are hospitality, education and healthcare.

An increase in such surveillance, however, may be double-edged, say privacy advocates. While a blanket coverage using CCTVs may give citizens a feeling of security, India’s rudimentary legislation around who can access these feeds is a problem. Some countries such as the UK and UAE have stricter guidelines on this. Law-enforcement agencies can access such feeds while following up on their investigations, says Supreme Court lawyer Karnika Seth, without procuring a warrant. “As long as it is for this purpose, it is within the purview of the law. However, with the new judgment on privacy, anything more would be a no-go area.”

The use of CCTV can potentially impinge on the rights of an individual, says Elonnai Hickok, who heads privacy research at the Centre for Internet and Society, an advocacy outfit in Bengaluru. “Technically speaking, the feed can reveal personal information about an individual, including identity, location and daily patterns. Because the feed captures individuals in public spaces, it is not possible for people to have an opt-out option. The access and use of the data are often unclear.” Regulations are starting to address the use of CCTV imagery in some places. The European Union’s General Data Protection Regulation, for example, has recognised that imagery that identifies an individual is personal data and thus requires lawful, fair and transparent processing.

The draft data protection bill by the Srikrishna committee also says CCTV imagery would be considered personal data. If CCTV cameras are put in place by a private actor, Hickok contends, they would need to adhere to the principles laid out in chapters II and III of the draft — which covers fair and reasonable processing, purpose limitation, collection limitation, lawful processing, notice, data quality, data storage limitation, accountability and consent.

For feeds used by the state for reasons such as public safety, the consent clause will not apply. But state actors will still need to adhere to the principles laid out in chapter II. If CCTVs are used for the purpose of prevention, detection, investigation and prosecution of a crime, it will be exempt from adhering to the requirements of the bill. However, this use must be backed by a law passed in Parliament and the data cannot be retained once its purpose has been met.

There are more legal restrictions if the CCTV application is integrated with capabilities that capture biometrics. "Clear responsibilities and reasons should be enunciated, the policies should be clearly documented and publicised and, importantly, the cost and benefits should be ascertained," Hickock argues. ¡§It is important to have technical safeguards like encryption and procurement guidelines.

Legal and privacy issues aside, the commercial aspect is clearly looking bright. Prama Hikvision, a Chinese-Indian joint venture, has invested Rs 100 crore in a factory in Bhiwandi to make 500,000 cameras a month. A second factory, possibly in Telangana, is expected to go on stream soon, with a monthly capacity of 1,50,000 units. "CCTVs have gone from being used by a sliver of companies, primarily banks and jewellers, to being adopted by a much broader audience," says Ashish Dhakan, MD and CEO, Prama Hikvision. "Our client list includes companies in the sectors of transportation, power, petroleum, oil and gas and retail."

Another trend market players have spotted is a shift from analog, which used tapes to record footage, to digital systems, where recording time and storage space are not major constraints. "We see continuous enhancement to megapixel (displays) from lowresolution, improved compression technology. This allows more data, more storage capacity, and overall lowering of cost for storage recording devices," says Sharad Yadav, general manager, Honeywell Building Technologies, India. Frost and Sullivan analyst Tiwari lists emerging offerings - including intelligent video surveillance, wireless systems and higher resolution of visuals - as features that will define the next-generation devices.

But digital also comes with some dangers. As CCTV cameras go from standalone devices to being digital and connected ones, experts say there is a risk of hacking. Hackers may also be able to use the network as a gateway. This could give hackers access to much more than just the camera feed. "Cybersecurity is a constant focus for us," says Holla of Axis Communications. "While no camera is hackproof, we believe we have built enough capabilities to react to these hacks and quickly release patches to secure them."

Others such as Hickok of CIS say more safeguards are required. "Technical safeguards like encryption and procurement guidelines are also important, as has been highlighted by the UK Information Commissioner's Office," she says. Keeping the cameras safe may be as important as safeguarding the lives these devices monitor.

For more details visit https://cis-india.org/internet-governance/news/economic-times-rahul-sachitanand-october-14-2018-sales-of-surveillance-cameras-are-soaring-raising-questions-about-privacy

Confidentiality of Communications and Privacy of Data in the Digital Age

praskrishna — 2018-10-28T06:02:07Z

On September 25, 2018, Elonnai Hickok participated in a side event Confidentiality of Communications and Privacy of Data in the Digital Age organized by INCLO and Privacy International at the Human Rights Council 39th ordinary session. Elonnai spoke on artificial intelligence and privacy.

For more details visit https://cis-india.org/internet-governance/news/confidentiality-of-communications-and-privacy-of-data-in-the-digital-age