{"id":385,"date":"2020-10-30T21:16:14","date_gmt":"2020-10-30T15:46:14","guid":{"rendered":"https:\/\/mcalaw.in\/?p=385"},"modified":"2021-02-02T12:41:40","modified_gmt":"2021-02-02T07:11:40","slug":"the-f-a-t-debate-in-indian-ai-regulation","status":"publish","type":"post","link":"https:\/\/mcalaw.in\/?p=385","title":{"rendered":"The F-A-T Debate in Indian AI Regulation"},"content":{"rendered":"\n

One of the earliest documents which laid out the Indian government\u2019s plans in relation to AI was the 2018 Report<\/a> of the DPIIT\u2019s Artificial Intelligence Task Force. The report noted that India\u2019s AI vision should prioritise problem solving over economic growth. This seems to be the common thread running across all subsequent Indian AI policy documents \u2013 ensuring the ethical application of AI for problem solving. But how ethical AI developed?<\/p>\n\n\n\n

This article crystallises observations from various important Indian policy documents on how AI regulation is likely to pan out in India to ensure the development of ethical AI. It first cursorily explores which government body heads the charge on AI regulation in India, after which it discusses the fairness, accountability, transparency or \u2018FAT\u2019 debate, which are the three guiding principles for the development of ethical AI, not only in India but globally. Lastly, the article examines the various alternatives proposed to imbue FAT in AI systems. To conclude, the author will posit his opinions on which of these alternatives are the best suited to achieve India\u2019s dream of becoming a one trillion dollar digital economy<\/a> by 2025, and an AI garage<\/a> for 40% of the world\u2019s population.<\/p>\n\n\n\n

Whose turf is it anyway?<\/strong><\/p>\n\n\n\n

Many ministries and government organisations have shown interest in taking the conversation around AI regulation forward. These include the Ministry of Electronics and Information Technology (\u201cMeitY<\/strong>\u201d) (here<\/a>), the Ministry of Commerce and Industry (here<\/a>), the Department of Telecommunications (which functions under the Ministry of Communications) (here<\/a> and as recently as September 2020 here<\/a>), and the NITI Aayog (here<\/a> and here<\/a>). Each ministry\/organisation has undertaken public consultations or released guidelines tailored to the needs of its specific sector.<\/p>\n\n\n\n

This has two implications. First, it indicates that AI regulation is likely to be sector driven. In fact, this is something which the NITI Aayog\u2019s latest document the \u2018Draft Document \u2013 Towards Responsible #AIforAll<\/a>\u2019 ( \u2018AIforAll draft\u2019) also recognises; it notes that AI regulation in several other jurisdictions has followed roughly a three tiered structure \u2013 (a) overarching guidelines or regulations established specifically for AI, (b) sector specific regulations which apply to AI, and (c) sector agnostic laws which may apply to AI [i]<\/a>. The top-level consequences of such sectoral regulation include possible regulatory conflict, but on the flipside, tailored sectoral regulation.<\/p>\n\n\n\n

The second implication deals with identifying the entity which will lead the charge on AI regulation, standard setting, infrastructure development etc. in India. Currently, the two entities most active in this segment are the NITI Aayog and the MeitY, and both have released multiple policy documents with pan-India and pan-sector applicability aimed at creating a national AI mission. Interestingly, the NITI Aayog, being only a government think tank, is not empowered to draft regulations for AI in India, but it may be able to influence certain standards in the AI ecosystem (much like it has been able to do with regards to the National Health Stack<\/a> and DEPA<\/a>, which is part of the India Stack architecture), to the MeitY\u2019s chagrin. In 2018, the NITI Aayog released an AI strategy for India (here<\/a>) (\u2018AI Strategy Document\u2019), in which it proposed a two-tiered structure to further India\u2019s AI research aspirations comprising (a) Centres of Research Excellence or COREs, focused \u201cpushing technology frontiers through creation of new knowledge<\/em>\u201d and (b) International Centers of Transformational AI or ICTAIs, focused on \u201cdeveloping and deploying application-based research\u2026with private sector collaboration<\/em>\u201d[ii]<\/a> and a dedicated cloud infrastructure for AI called AIRAWAT [iii]<\/a>. Soon after, the NITI Aayog sought<\/a> INR 7,000 crores from the government to establish an Indian AI Mission, which created some discomfort within the MeitY, which was drafting its own plan for a INR 400 crores AI programme<\/a> for India. These concerns were exacerbated in September 2019, after reports emerged<\/a> that the finance ministry had cleared the NITI Aayog\u2019s INR 7,500 crore proposal to set-up an AI framework for India over the subsequent three years. To address this, in mid-October 2019, the government reportedly<\/a> formed a committee to (i) resolve overlaps between MeitY and NITI Aayog\u2019s AI plans; and (ii) specify roles of different agencies to fast-track implementation of the AI mission. The committee, which has representation from the Department of Science and Technology, MeitY and NITI Aayog, is yet to come out with its report, leading to growing regulatory uncertainty.<\/p>\n\n\n\n

FAT Machine Learning \u2013 The way ahead<\/strong><\/p>\n\n\n\n

Any person interested in AI regulation, is sure to have run into the acronym FAT<\/a> or FATE<\/a>. This stands for Fairness, Accountability and Transparency (and Ethics) in AI, which embodies the understanding that any regulation around AI would be targeted to ensuring these three elements, such that AI-based solutions can be deployed in a safe, responsible, accountable, and ethical manner.<\/p>\n\n\n\n

Fairness<\/strong><\/p>\n\n\n\n

Fairness means that an AI-based solution should not be biased towards or against a certain segment of the population. Since data sets used to train AI are picked up en masse and are not usually manually collated, they are rarely truly neutral. This has been observed in multiple government documents[iv]<\/a> on AI, most recently in the AIforAll draft. itigate discrimination and harm to individuals.[xxxv]<\/a><\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

Substantial work remains to be done in the field of AI. Any government led hard regulation is only likely to fetter innovation in this field. Fortunately, both the Niti Aayog and MeitY have clarified that they are unlikely to mandate algorithm code sharing to achieve FAT in AI systems, which comes as some respite to innovators in the field. Industry responses to the personal data regulation<\/a> and non-personal data regulation<\/a> are abound with arguments that mandatory data sharing, data access, and hard regulation will impact innovation detrimentally. In light of the same, the MeitY\u2019s approach, which tends to self-regulation, stakeholder-led development of shared standards, and an overall light touch regulatory approach is commendable and should be maintained. Further, any form of regulation must also promote private sector involvement which would ensure the development of neutral, efficient and up-to-date technical solutions to some of the problems highlighted above. This is because private sector participants are in the weeds of things, which not only gives them visibility into practical problems with algorithms, but also presents them with an inherent incentive to solve for these problems to increase the credibility of their offerings and their brand. Further, they have the human capital to achieve this. AI regulation in India is only in its nascent stages at present, and it will be interesting to see how it develops going forward.<\/p>\n\n\n\n