We are anonymous, we are legion

AI is biased, you’ll see if you Google ‘hands’

Rajmohan Sudhakar — 2019-08-26T23:53:38Z

As it is, the world is unfair. The question now is, do we want automated tech to be unfair too? As we build more and more AI-dependent smart digital infrastructure in our cities and beyond, we have pretty much overlooked the emerging character of artificial intelligence that would have a profound bearing on our nature and future.

The article by Rajmohan Sudhakar was published in Deccan Herald on August 25, 2019. Radhika Radhakrishnan was quoted.

Are we happy with algorithms making decisions for us? Naturally, one would expect the algorithm to possess discretion. Herein lies the dilemma. Do you trust an AI algorithm? Though an algorithm can evolve over time drawing on the nature and accuracy of the dataset, it shall nevertheless pick up the prejudices and biases it is exposed to.

“Questions on fairness arise at multiple stages of AI design. For instance, who has access to large datasets? The private sector in India. There may not be data at all on marginalised communities while there can be excessive surveillance data on targeted communities. Historic biases in datasets add up: widely used leading datasets of word embeddings associate women as homemakers and men as computer programmers. Focus on FAT (Fairness, Accountability and Transparency) is crucial,” says Radhika Radhakrishnan, programme officer at The Centrefor Internet and Society.

For example, a whopping 90% of the Wikipedia editors are men.

As AI is expected to add 15 trillion US dollars by 2030 to the global economy, at present, the data it relies upon comes from a few nations (45% from the US) while a major chunk of users are elsewhere. As it is vital to any social mechanism, diversity will be key if we are to reap the true benefits of AI. Or else, a non-diverse data set or a programmer crafting an algorithm could chart the most unpleasant course.

“Research recommends the inclusion of social scientists in AI design and ensuring they have decision-making power. The AI Now Institute, for instance. However, there is a dearth of social scientists working on AI. In India, we ignore the social impact of AI in favour of the purely technical solutions of computer scientists. Lack of women, gender-queers, and individuals from under-represented communities reflects poor diversity within the AI industry,” Radhakrishnan points out.

The G20 adopted AI Principles in June, which stressed “AI actors should respect the rule of law,human rights and democratic values, throughout the AI system life-cycle. These include freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality,diversity, fairness, social justice, and internationally recognised labour rights.”

The UK recently set up the Centre for Data Ethics and Innovation. Canada and France are spearheading the International Panel on Artificial Intelligence (IPAI) on the sidelines of the G7summit. Meanwhile, India and France agreed on a slew of measures to advance cooperation ondigital tech. Of course, the EU’s General Data Protection Regulation (GDPR) is a promising start.

All of that is well and welcome. But what such efforts and international bodies could achieve in reality is to be seen as questions loom large over private corporations that own tech exercising clout, henceforth leaving AI vulnerable to manipulation.

“To achieve this, panel members will need to be protected from direct or indirect lobbying by companies, pressure groups and governments — especially by those who regard ethics as a brake on innovation. That also means that panel members will need to be chosen for their expertise, not for which organisation they represent,” reads an August 21 editorial in Nature journal on IPAI.

Whatever one may do to de-bias AI, much damage is done already. Try a google search for images of hands. How many black/brown hands do you see? There you go.

“Whose needs are being reflected in AI — those of the poor or those of the big tech looking to‘dump’ their products in an easily exploitable market? Instead of asking, what is the AI solution,we should be wondering, is an AI-based solution necessary in this case?” adds Radhakrishnan.

Where are the big tech located? In the United States. When a white male sitting in that country crafts an algorithm based on a bought dataset, for the benefit of an aboriginal community in the Amazon, something’s amiss.

“Engineers and data scientists who design algorithms are often far removed from the socioeconomic contexts of the people they are designing the tools for. So, they reproduce ideologies that are damaging. They end up reinforcing prejudices. Direct engagement is rare. Engineers should actively and carefully challenge their biases and assumptions by engaging meaningfully with communities to understand their histories and needs,” explains Radhakrishnan.

The march of AI cannot be stopped as more and more datasets get integrated. An ethical approach to computer science and engineering should begin from our institutions of excellence.

“Computer science and engineering disciplines at the undergraduate level teach AI as a purelytechnical subject, not as an interdisciplinary subject. Engineers should be trained in the socialimplications of the systems they design. Technology inevitably re􀁻ects its creators, consciousor not. Therefore, deeper attention to the social contexts of AI and the potential impact of suchsystems when applied to human populations should be incorporated to university curricula,”notes Radhakrishnan.

For more details visit https://cis-india.org/internet-governance/news/deccan-herald-rajmohan-sudhakar-august-25-2019-ai-is-biased-you-see-if-you-google-hands

AI in the Banking and Finance Industry in India

Saman Goudarzi, Elonnai Hickok and Amber Sinha — 2018-06-19T11:48:39Z

This is a draft report that seeks to map the present state of use of AI in the banking and financial sector in India.

This draft report was prepared by Saman Goudarzi, Elonnai Hickok and Amber Sinha. It was edited by Shyam Ponappa. Mapping was done by Shweta Mohandas. Pranav M Bidare, Sidharth Ray, and Aayush Rathi provided research assistance in preparing this report.

Executive Summary

In the last couple of years, the finance and banking sectors in India have increasingly deployed and implemented AI technologies. Such technologies are being implemented for front-end and back end processes – offering solutions for both financial and business management operations. At the moment, the AI landscape appears to be overwhelmingly populated by natural language processing and natural language generation technologies culminating in numerous chatbot initiatives by various banking and financial actors. Arguably more significant – but less documented – is the usage of said technologies for financial decision making on a variety of issues including, credit-scoring, transactions, wealth and risk management, and fraud detection. These trends are largely facilitated by technology service companies – both large-scale firms and startups – that either work with established banking and financial institutions to deploy AI technologies or develop and offer their own financial services directly to consumers.

This draft report seeks to map the present state of use of AI in the banking and financial sector in India. In doing so, it explores:

Uses: What is the present use of AI in banking and finance? What is the narrative and discourse around AI and banking/finance in India?
Actors: Who are the key stakeholders involved in the development, implementation and regulation of AI in the banking/finance sector?
Impact: What is the potential and existing impact of AI in the banking and finance sectors?
Regulation: What are the challenges faced in policy making around AI in the banking and finance sectors?

The draft report first offers an overview of the ways in which AI is being used in the sector. This is followed by an examination of existing challenges to the adoption of AI and the significant legal and ethical concerns that need to be considered in light of these trends. Lastly, the draft report draws attention to a number of key government actions and initiatives surrounding AI related to the banking and finance industry, discusses challenges to the adoption and implementation of AI and articulates recommendations towards addressing the same.

Download the draft report here

19th June Update: This case study has been modified to remove interview quotes, which are in the process of being confirmed. The link above is the latest draft of the report.

For more details visit https://cis-india.org/internet-governance/blog/ai-in-banking-and-finance

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.

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^{^[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

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^{^[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/

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^{^[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

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^{^[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

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^{^[29]} Id.

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

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^{^[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

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^{^[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

AI in Healthcare

Admin — 2019-09-19T16:15:24Z

The Center for Information Technology and Public Policy (CITAPP) and the International Institute of Information Technology Bangalore (IIITB) invited Radhika Radhakrishnan for a talk at IIIT-Bangalore on September 13, 2019.

In her talk, she critically questioned the dominant narrative of “AI for social good” that has been widely adopted by various stakeholders in India (including the private sector, non-profits, and the Indian State) from a feminist standpoint. Specific to healthcare in India, such a narrative has been employed towards solving development challenges (such as a shortage of medical practitioners in remote regions of the country) through the introduction of AI applications targeted towards the sick-poor. Through her research and fieldwork, she analysed the layers of expropriation and experimentation that come into play when AI technologies become a method of using 'diverse' bodies and medical records of the sick-poor as ‘data’ to train proprietary AI algorithms at a low cost in the absence of effective State regulatory mechanisms. She argued that structural challenges (such as lack of incentives for medical practitioners to join public healthcare) get reframed into opportunities to substitute labour (people) by capital (technology) through innovation of “spectacular technologies” such as AI. Throughout the talk, she also highlighted the methodologies she used to conduct this research.

For more details visit https://cis-india.org/internet-governance/news/ai-in-healthcare

AI for Social Good Summit

Admin — 2018-12-25T01:02:01Z

Arindrajit Basu was a speaker at the event co-organized by Google AI and United Nations ESCAP on December 13, 2018 in Bangkok, Thailand.

Arindrajit spoke at the panel " How can governments use AI in Public Service Delivery" along with Malavika Jayaram, Jake Lucci,Punit Shukla,Simon Schmooly and Gal Oren. He presented CIS research on AI in agriculture in Karnataka-which will be published as part of a compendium documenting case studies worldwide soon.

Click to read more

For more details visit https://cis-india.org/internet-governance/news/unescap-and-google-ai-december-13-bangkok-ai-for-social-good-summit

AI for Social Good

praskrishna — 2017-03-15T01:22:42Z

AI in Asia event was organised by the Digital Asia Hub on March 6 and 7, 2017 in Tokyo, Japan. The event partners were Waseda University, the Ministry of Internal Affairs and Communications, NICT, NTT and the Japan Society of Information and Communication Research. Udbhav Tiwari attended the event.

Udbhav Tiwari presented a small (2 minute) lighting talk on the Autonomous Weapons Design section as a part of the 'Interactive Exercise on Next Steps for the Academic Community session', with the IEEE Ethically Aligned Design document as a guiding note. Read the executive summary here. See the workshop agenda here.

For more details visit https://cis-india.org/internet-governance/news/ai-for-social-good

AI for India Summit

Admin — 2019-04-04T16:31:21Z

Shweta Mohandas attended this event held on March 26, 2019 at Leela Palace in Bengaluru. The event was organized by Facebook.

For more info, click here.

For more details visit https://cis-india.org/internet-governance/news/facebook-ai-for-india-summit

AI for Good Workshop

Admin — 2019-06-05T14:47:27Z

Pranav Manjesh Bidare attended a workshop on AI for Good, organised by Swissnex India, and Wadhwani AI in Bangalore on May 22, 2019.

The workshop was a forerunner to the AI for Good Global Summit. More recommendations can be made at https://www.policykitchen.com/group/19/stream

For more details visit https://cis-india.org/internet-governance/news/ai-for-good-workshop

AI for Good

Shweta Mohandas and Saumyaa Naidu — 2019-10-13T05:32:28Z

CIS organised a workshop titled ‘AI for Good’ at the Unbox Festival in Bangalore from 15th to 17th February, 2019. The workshop was led by Shweta Mohandas and Saumyaa Naidu. In the hour long workshop, the participants were asked to imagine an AI based product to bring forward the idea of ‘AI for social good’.

The report was edited by Elonnai Hickok.

The workshop was aimed at examining the current narratives around AI and imagining how these may transform with time. It raised questions about how we can build an AI for the future, and traced the implications relating to social impact, policy, gender, design, and privacy.

Methodology

The rationale for conducting this workshop in a design festival was to ensure a diverse mix of participants. The participants in the workshop came from varied educational and professional backgrounds who had different levels of understanding of technology. The workshop began with a discussion on the existing applications of artificial intelligence, and how people interact and engage with it on a daily basis. This was followed by an activity where the participants were provided with a form and were asked to conceptualise their own AI application which could be used for social good. The participants were asked to think about a problem that they wanted the AI application to address and think of ways in which it would solve the problem. They were also asked to mention who will use the application. It prompted participants to provide details of the AI application in terms of the form, colour, gender, visual design, and medium of interaction (voice/ text). This was intended to nudge the participants into thinking about the characteristics of the application, and how it will lend to the overall purpose. The form was structured and designed to enable participants to both describe and draw their ideas. The next section of the form gave them multiple pairs of principles. They were asked to choose one principle from each pair. These were conflicting options such as ‘Openness’ or ‘Proprietary’, and ‘Free Speech’ or ‘Moderated Speech’. The objective of this section was to illustrate how a perceived ideal AI that satisfies all stakeholders can be difficult to achieve, and that the AI developers at times may be faced with a decision between profitability and user rights.

Participants were asked to keep their responses anonymous. These responses were then collected and discussed with the group. The activity led to the participants engaging in a discussion on the principles mentioned in the form. Questions around where the input data to train the AI would come from, or what type of data the application will collect were discussed. The responses were used to derive implications on gender, privacy, design, and accessibility.

Responses

Analysis

Even as the responses were varied, they had a few key similarities and observations.

Participants’ Familiarity with AI

The participants’ understanding of AI was based on what they read and heard from various sources. While discussing the examples of AI, the participants were familiar with not just the physical manifestation of AI such as robots, but also AI software. However when asked to define an AI the most common explanations were, bots, software, and the use of algorithms to make decisions using large amounts of data. The participants were optimistic of the way AI could be used for social good. However, some of them showed concern about the implications on privacy.

Perception of AI Among Participants

With the workshop, our aim was to have the participants reflect on their perception of AI based on their exposure to the narratives around AI by companies and the government.

The participants were given the brief to imagine an AI that could solve a problem or be used for social good. Most participants considered AI to be a positive tool for social impact. It was seen as a problem solver. The ideas conceptualised by the participants varied from countering fake news, wildlife conservation, resource distribution, and mental health. This brought to focus the range of areas that were seen as pertinent for an AI intervention. Most of the responses dealt with concerns that affect humans directly, the one aimed at wildlife conservation being the only exception.

On being asked, who will use the AI application, it was interesting to note that all the responses considered different stakeholders such as individuals, non profits, governments and private companies to be the end user. However, it was interesting that through the discussion the harms that might be caused by the use of AI by these stakeholders were not brought up. For example, the use of AI for resource distribution did not take into consideration the fact that the government could provide unequal distribution based on the existing biased datasets. Several of the AI applications were conceptualised to work without any human intervention. For example, one of the ideas proposed was to use AI as a mental health counsellor which was conceptualised as a chatbot that would learn more about human psychology with each interaction. It was assumed that such a service would be better than a human psychologist who can be emotionally biased. Similarly, while discussing the idea behind the use of AI for preventing the spread of fake news, the participant believed that the indication coming from an AI would have greater impact than one coming from a human. They believed that the AI could provide the correct information and prevent the spread of fake news. By discussing these cases we were able to highlight that the complete reliance on technology could have severe consequences.

Form and Visual Design of the AI Concepts

In most cases, the participants decided the form and visual design of their AI concepts keeping in mind its purpose. For instance, the therapy providing AI mentioned earlier, was envisioned as a textual platform, while a ‘clippy type’ add on AI tool was thought of for detecting fake news. Most participants imagined the AI application to have a software form, while the legal aid AI application was conceptualised to have a human form. This revealed that the participants perceived AI to be both a software and a physical device such as a robot.

Accessibility of the Interfaces

The purpose of including the type of interface (voice or text) while conceptualising the AI application was to push the participants towards thinking about accessibility features. We aimed to have the participants think about the default use of the interface, both in terms of language and accessibility. The participants though cognizant of the need to have a large number of users, preferred to have only textual input into the interface, not anticipating the accessibility concerns.

The choices between access vs cost, and accessibility vs scalability were also questioned by the participants during the workshop. They enquired about the meaning of the terms as well as discussed the difficulty in having an all inclusive interface. Some of the responses consisted only of text inputs, especially for sensitive issues involving interactions, such as for therapy or helplines. This exercise made the participants think about the end user as well as the ‘AI for all’ narrative. We decided to add these questions that made the participants think about how the default ability, language, and technological capability of the user is taken for granted, and how simple features could help more people interact with the application. This discussion led to the inference that there is a need to think about accessibility by design during the creation of the application and not as an afterthought.

Biases Based on Gender

We intended for the participants to think about the inherent biases that creep into creating an AI concept. These biases were evident from deciding identifiably male names, to deciding a male voice when the application needed to be assertive, or a female voice and name for when it was dealing with school children. Most of the other participants either did not mention the gender or they said that the AI could be gender neutral or changeable.

These observations are also revealing of the existing narrative around AI. The popular AI interfaces have been noted to exemplify existing gender stereotypes. For example, the virtual assistants were given female identifiable names and default female voices such as Siri, Alexa, and Cortana. The more advanced AI were given male identifiable names and default male voices such as Watson, Holmes etc. Although these concerns have been pointed out by several researchers, there needs to be a visible shift towards moving away from existing gender biases.

Concerns around Privacy

Though the participants were aware of the privacy implications of data driven technologies, they were unsure of how their own AI concept could deal with questions of privacy. The participants voiced concerns about how they would procure the data to train the AI but were uncertain about their data processing practices. This included how they would store the data, anonymise the data, or prevent third parties from accessing it. For example, during the activity, it was pointed out to the participants that there would be sensitive data collected in applications such as therapy provision, legal aid for victims of abuse, and assistance for people with social anxiety. In these cases, the participants stated that they would ensure that the data was shared responsibly, but did not consider the potential uses or misuses of this shared data.

Choices between Principles

This part of the exercise was intended to familiarise the participants with certain ethical and policy questions about AI, as well as to look at the possible choices that AI developers have to make. Along with discussing the broader questions around the form and interface of AI, we wanted the participants to also look at making decisions about the way the AI would function. The intent behind this component of the exercise was to encourage the participants to question the practices of AI companies, as well as understand the implications of choices while creating an AI. As the language in this section was based on law and policy, we spent some time describing the terms to the participants. Even as some of the options presented by us were not exhaustive or absolute extremes, we placed this section to demonstrate the complexity in creating an AI that is beneficial for all. We intended for the participants to understand that an AI that is profitable to the company, free for people, accessible, privacy respecting, and open source, though desirable may be in competition with other interests such as profitability and scalability.

The participants were urged to think about how decisions regarding who can use the service, how much transparency and privacy the company will provide, are also part of building an AI. Taking an example from the responses, we talked about how having a closed proprietary software in case of AI applications such as providing legal aid to victims of abuse would deter the creation of similar applications. However, after the terms were explained, the participants mostly chose openness over proprietary software, and access over paid services.

Conclusion

The aim of this exercise was to understand the popular perception of AI. The participants had varied understanding of AI, but were familiar with the term. They also knew of the popular products that claim to use AI. Since the exercise was designed for people as an introduction to AI policy, we intended to keep questions around data practices out of the concept form. Eventually, with this exercise, we, along with the participants, were able to look at how popular media sells AI as an effective and cheaper solution to social issues. The exercise also allowed the participants to understand certain biases with gender, language, and ability. It also shed light on how questions of access and user rights should be placed before the creation of a technological solution. New technologies such as AI are being featured as problem solvers by companies, the media and governments. However, there is a need to also think about how these technologies can be exclusionary, misused, or how they amplify existing socio economic inequities.

[1]. https://www.bizjournals.com/sanfrancisco/news/2019/08/26/maximizing-the-potential-of-ai-starts-with-trust.html

[2]. https://qz.com/1023448/if-youre-not-a-white-male-artificial-intelligences-use-in-healthcare-could-be-dangerous/

[3]. https://www.vox.com/the-goods/2018/11/29/18118469/instagram-accessibility-automatic-alt-text-object-recognition

[4]. https://www.theguardian.com/pwc-partner-zone/2019/mar/26/why-are-virtual-assistants-always-female-gender-bias-in-ai-must-be-remedied

For more details visit https://cis-india.org/internet-governance/blog/ai-for-good-event-report-on-workshop-conducted-at-unbox-festival

AI and Manufacturing and Services in India: Looking Forward

Shweta Mohandas and Pranav M. Bidare — 2018-02-14T11:13:56Z

This Report provides an overview of the proceedings of the Roundtable on Artificial Intelligence (AI) in Manufacturing and Services: Looking Forward (hereinafter referred to as ‘the Roundtable’), conducted at The Energy Resource Institute (TERI), in Bangalore on January 19, 2018.

Event Report: Download (PDF)

The Roundtable comprised of participants from different sides of the AI and manufacturing and services spectrum including practitioners, representatives from multinational companies, think tanks, academicians, and researchers. The Roundtable discussed various questions regarding AI in the manufacturing and services industry in India.

The round of discussions began with initial observations from the in progress research that the Centre for Internet and Society (CIS) is undertaking, on the use of AI in manufacturing and services. Some of the uses of AI that the research had thus far identified across various sectors included AI platforms in IT services for accurate forecasting for businesses, AI driven automation of routine tasks in manufacturing and production, and AI driven analytics for forecasting in the agriculture sector. The discussion then proceeded to the benefits of using AI - including efficient and effective results, precision, and automation of repetitive maintenance tasks. The draft research also acknowledges that although the use of AI is beneficial in many ways, there are also some key concerns around job displacement, privacy, lack of awareness, and a needed capacity to fully understand and use new AI technologies. The draft research also identified a few key AI initiatives in India, such as Wipro Holmes, TCS Ignio, and G.E, that were providing solutions to help automating software maintenance tasks and helping in the smooth working of SAP (Systems, Applications & Products) operations. Innovative uses of AI in areas such as crop production (M.I.T.R.A.) and dairy optimization (StellApps) were also identified.

To understand the present state of AI and impact of the same, the session was opened to discussion on the following questions: See the full report here.

For more details visit https://cis-india.org/internet-governance/blog/ai-and-manufacturing-and-services-in-india-looking-forward

Ahead of hosting Modi, Facebook rebrands internet.org as Free Basics

praskrishna — 2015-10-18T14:21:52Z

Hinting at what could be vital points of discussion when Prime Minister Narendra Modi visits Facebook founder Mark Zuckerberg on Sunday, the social media giant has rebranded its internet access enabling platform Internet.org as Free Basics.

The article was published by Business Standard on September 26, 2015. Pranesh Prakash was quoted.

This was announced by Chris Daniels, vice-president of Internet.org, at a press meet in Menlo Park on Friday. Zuckerberg confirmed the same and wrote on his Facebook wall.

Facebook has opened up its Free Basics platform, which means any app developer can now include their services on it. “This gives people the power to choose what apps they want to use.” Zuckerberg in his post also said the company has improved the security and privacy of Internet.org, which will support HTTPS web services as well. “Connectivity isn't an end in itself. It’s what people do with it that matters. We hope the improvements we've made help even more people get connected — so that our whole global community can benefit together,” Zuckerberg said in his post, in which he quoted the example of a soybean farmer from Maharashtra, Asif Mujhawar, who uses parenting app BabyCenter for free through Internet.org.

This is a significant move by Facebook, considering the backlash it had from various quarters in India following debates on net neutrality. Internet.org is an open platform by Facebook across 19 developing countries, including India, to enable easy access of selected apps and app-based services to people at zero cost. In India, it had partnered with Reliance Communications to offer free access to about 30 websites.

“One of the concerns was calling the service ‘Internet.org’, despite it representing only a tiny sliver of the Internet,” said Pranesh Prakash, policy director at the centre for Internet and Society, a nonprofit entity to promote safe internet access in the country.

He said by removing the Internet word, Facebook is now talking of its own larger internet affordability project and allowing app developers to build apps and host it on the Free Basic platform. “This gives people the power to choose what apps they want to use,” Prakash said.

For more details visit https://cis-india.org/internet-governance/news/business-standard-september-26-2015-ahead-of-hosting-modi-facebook-rebrands-internet-dot-org-as-free-basics

Ahead of data protection law roll out, experts caution that it shouldn't limit collection and use of data

Admin — 2018-01-02T15:20:48Z

With India planning to roll out a new data protection regime following the landmark Supreme Court judgment upholding right to privacy as fundamental right, experts have cautioned that the new law should not limit collection and use of data.

The article was published by First Post on October 12, 2017. Sunil Abraham was quoted.

"The new data protection law should have data-driven innovation at its core," said Kamlesh Bajaj, Founder-CEO, Data Security Council of India (DSCI).

"It should not limit data collection and use, but limit harm to citizens," Bajaj added at a seminar on "Data Protection and Privacy" organised by non-profit industry body Internet and Mobile Association of India (IAMAI).

In a major boost to individual freedom, the Supreme Court in August declared that right to privacy was a fundamental right and protected as an intrinsic part of life and personal liberty and freedoms guaranteed by the Constitution.

"The Supreme Court judgment calls for production of a new law," said Sunil Abraham, Executive Director of Bangaluru-based research organisation, Centre for Internet and Society (CIS).

The experts noted that the Supreme Court judgment remains meaningless for digital Indians without a proper data protection law in place as all other existing laws, such as the Information Technology Act, 2000, do not adequately address the question of right to privacy.

Recognising the importance of data protection and keeping personal data of citizens secure and protected, the Ministry of Electronics and Information Technology (MeitY) on 31 July, constituted a Committee of Experts under the chairmanship of its former judge Justice BN Srikrishna to study and identify key data protection issues and recommend methods for addressing them. The committee will also suggest a draft Data Protection Bill.

"While the regulator should be given tools to make companies behave better, it should not start with harsh punitive actions," Abraham noted, adding that big fines could challenge the very logic of regulation.

In a question to whether a robust data protection regime should come in conflict with issue such as national security, he said that lawmakers should find a way to maximise both imperatives.

"Surveillance is like salt in cooking. It is necessary, but in limited quantity," he added.

Participating in a chat with Google's Public Policy Director Chetan Krishnaswamy at the event, MP Rajeev Chandrasekhar, however, said that regulation should start with the process of data collection itself and consumers cannot be expected to demonstrate harm or inappropriate use of their data to enjoy the right to privacy.

"It should not be a free run for companies to mine consumer data," the independent Rajya Sabha member said.

He emphasised that the process of formulating a data protection law is as important as the law itself and all stakeholders should be able to openly put forward their views and apprehensions and it is only with such a consultative process that the opportunities for the technology space can be safeguarded.

For more details visit https://cis-india.org/internet-governance/news/first-post-october-12-2017-ahead-of-data-protection-law-roll-out-experts-caution-that-it-shouldnt-limit-collection-and-use-of-data

Agriculture, ICT and Community

radha — 2011-04-05T04:29:16Z

Sampada Foundation in collaboration with Centre for Internet and Society, Bangalore and Institution of Agricultural Technologists present: 'Agriculture, ICT and Community'

Sampada Foundation in collaboration with Centre for Internet and Society, Bangalore and Institution of Agricultural Technologists present:
Agriculture, ICT and Community on the 21st of September, 2009 at 10am. The agenda for the day will be as follows:

Sampada Community - an overview (15 minutes)
Introduction: Krushi Sampada (15 minutes)
Launch of e-book - By Nagesh Hegde
Talk by Addoor Krishna Rao: “Transformations in Agricultural Sector”
Discussion: Agriculture, ICT and Community

There will be no entry or registration fees. All are invited and are welcome to bring their friends along.

For more details visit https://cis-india.org/events/agriculture-ict-and-community

After the Lockdown

Shyam Ponappa — 2020-04-09T10:05:49Z

This post was first published in the Business Standard, on April 2, 2020.

This is a time when, as the authorities deal with a lockdown, there needs to be an equal emphasis on providing for large numbers of people without the money for food and necessities, while the rest of us wait it out. Hard as it is, an MIT scholar writes that after the Spanish flu in 1918, cities that restricted public gatherings sooner and longer had fewer fatalities, and emerged with stronger economic growth.1 It is likely that costs and benefits vary with economic and social capacity, and we may have a harder time with it here. Going forward, government action to help provide relief, rehabilitate people and deal with loss needs to be well planned, including targeting aid to the urban and displaced poor.2

As important now as to ensure the lockdown continues is to plan on how to revive productive activity and the economy, and restore public confidence. A systematic approach will likely yield better results.

A major element of the recovery plan is steps such as liberal credit and amortisation terms, perhaps much more than the three-month extension the Reserve Bank of India (RBI) has announced. A primary purpose is the re-initiation of large-scale activities such as construction, of which there are reportedly about 200,000 large projects around the country. These have to be nursed back to being going concerns. The RBI may need to consider doing more, including lowering rates.

An ominous development that has grown as the economy slowed is financial stress that could swell non-performing assets (NPAs). At the half-year ending September 2019, about half of non-financial large corporations in India, excluding telecom, showed financial stress (see table).

Source: Krishna Kant: "Coronavirus shutdown puts Rs 15-trillion debt at risk, to impact finances", BS, March 30, 2020:

https://www.business-standard.com/article/markets/coronavirus-shutdown-puts-rs-15-trillion-debt-at-risk-to-impact-finances-120032901036_1.html

These include some of India’s largest companies, producing power, steel, and chemicals. The 201 companies have total debt of nearly Rs 15 trillion, more than half of all borrowings. There is also the debt overhang of the National Highways Authority of India, and of the telecom companies. Ironically, the telecom companies are our lifeline now, despite having nearly collapsed under debt because of ill-advised policies in the past, which have still not changed. Perhaps our obvious dependence telecom services now will spark well conceived, convergent policies for this sector, so that we can function effectively.

A start with immediate changes in administrative rules for 60GHz, 70-80GHz, and 500-700MHz wireless use, modelled on the US FCC regulations as was done for the 5GHz Wi-Fi in October 2018, could change the game. It will provide the opportunity in India for the innovation of devices, their production, and use, possibly unleashing this sector. This can help offset our reliance on imported technology and equipment. However, such changes in policies and purchasing support have eluded us thus far. Now, the only way our high-technology manufacturers can thrive is to succeed internationally, in order to be able to sell to the domestic market. Imagine how hard that might be, and you begin to get an inkling of why we have few domestic product champions, struggling against odds in areas such as optical switches, networking equipment, and wireless devices. For order-of-magnitude change, however, structural changes need to be worked out in consultation with operators in the organisation of services through shared infrastructure.

For the longer term, a fundamental reconsideration for allocating resources is needed through coherent, orchestrated policy planning and support. What the government can do as a primary responsibility, besides ensuring law and order and security, is to develop our inadequate and unreliable infrastructure, including facilities and services that enable efficient production clusters, their integrated functioning, and skilling. For instance, Apple’s recent decision against moving iPhone production from China to India was reportedly because similar large facilities (factories of 250,000) are not feasible here, and second, our logistics are inadequate. Such considerations should be factored into our planning, although Apple may well have to revisit the very sustainability of the concept of outsize facilities that require the sort of repressive conditions prevailing in China. However, we need not aim for building unsustainable mega-factories. Instead, a more practical approach may be to plan for building agglomerations of smaller, sustainable units, that can aggregate their activity and output effectively and efficiently. Such developments could form the basis of numerous viable clusters, and where possible, capitalise on existing incipient clusters of activities. Such infrastructure needs to be extended to the countryside for agriculture and allied activities as well, so that productivity increases with a change from rain-fed, extensive cultivation to intensive practices, with more controlled conditions.

The automotive industry, the largest employer in manufacturing, provides an example for other sectors. It was a success story like telecom until recently, but is now floundering, partly because of inappropriate policies, despite its systematic efforts at incorporating collaborative planning and working with the government. It has achieved the remarkable transformation of moving from BS-IV to BS-VI emission regulations in just three years, upgrading by two levels with an investment of Rs 70,000 crore, whereas European companies have taken five to six years to upgrade by one level. This has meant that there was no time for local sourcing, and therefore heavy reliance on global suppliers, including China. While the collaborative planning model adopted by the industry provides a model for other sectors, the question here is, what now. In a sense, it was not just the radical change in market demand with the advent of ridesharing and e-vehicles, but also the government’s approach to policies and taxation that aggravated its difficulties.

Going forward, policies that are more congruent in terms of societal goals, including employment that support the development of large manufacturing opportunities, need to be thought through from a perspective of aligning and integrating objectives (in this case, transportation). Areas such as automotive and other industries for the manufacture of road and rail transport vehicles need to be considered from the perspective of reconfiguring the purpose, flow, and value-added, to achieve both low-cost, accessible mass transport, and vehicles for private use that complement transportation objectives as also employment and welfare.

Systematic and convergent planning and implementation across sectors could help achieve a better revival.

Shyam (no space) Ponappa at gmail dot com

1: https://www.reuters.com/article/us-health-coronavirus-usa-reopen-analysi/the-u-s-weighs-the-grim-math-of-death-vs-the-economy-idUSKBN21H1B4

2: https://www.financialexpress.com/opinion/the-coronavirus-lockdown-and-indias-urban-vulnerables/1915316/

For more details visit https://cis-india.org/internet-governance/blog/after-the-lockdown

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