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LEGAL ISSUES AND COMPUTATIONAL MEASURES AT THE CROSS-SECTION OF AI, LAW AND POLICYAuthor: SHUBHAM Rathi Date: 2019-05-14 Report no: IIIT/TH/2019/34 Advisor:Aniket Alam AbstractThe rise in the use and penetration of Artificial Intelligence/ Machine Learning in everyday life has led to many complex issues in Law and Policy. Chiefly, the governance of AI entities under the ambit of law. This thesis explores two issues in detail: General Data Protection Regulation (GDPR) floated ‘right to explanation’ and the problematic nature of Intellectual Property for AI.With the advent of GDPR, the domain of explainable AI and model interpretability has gained addedimpetus. Methods to extract and communicate visibility into decision-making models have become a legal necessity. The GDPR terms this as the ‘Right to Explanation’: Any person affected by the decision of an autonomous decision making system is empowered to seek an answer for the same. This is especially relevant in 21st century scenarios wherein AI systems are used in determining the credit worthiness, evaluating stocks, recommending news, screen candidates for jobs etc. In all these scenarios, the end user suffers a social, economic or a psychological impact by the discretion of the machine. Most of these AI systems function with complex black box models which have very little visibility in its inner workings. Even its developers might not be fully aware as to why and how these self learning systems adapt and react to new data either due to their evolving nature or its sheer complexity. In such a scenario, it is apt that appropriate justifications for the actions of the machine be generated. This thesis explores two such methods of generating explanations: an Antehoc technique conceptualized atop the existing SUMO Ontology and a Posthoc method constructed out of model interpretability techniques which generate contrastive and counterfactual explanations. Our computational explanations are a remedy to the challenge posed by the ‘Right to explanation’ and also serve in making the blackbox models more fair, reliable and most importantly understandable for the end user. The next part of the thesis focuses on the problematic nature of Intellectual Property (Copyright & Patents) for AI. With the ‘third wave’ of Artificial Intelligence, there is a massive revival and upsurge in AI related product development. An important entity behind the AI architecture, the neural network needs to be studied carefully that adequate protection for its innovation can be secured. A key feature of the Neural Network, the Neural weights hold the inferential rules and knowledge, thus are a new way to embody knowledge and information, a new form of intellectual property to which IP laws will have to adapt. We present our discussion that sheds light on the nature of this innovation and brings to context why it is relevant to secure Intellectual Property for Neural Weights. We also rebase our arguments in the backdrop of the debates that were set off on this same topic in 1990. Our research traces the shape of this problem ever since its conception and brings to the fore the newer and expanded notions behindNeural Networks, AI and their place in the Copyright laws. We also propose tentative solutions and the hurdles in implementing them as part of the study on streamlining the rough contours of the subject. As with Copyright, Patent Laws for AI are also predated. The Patent law is silent about tackling cases when an AI develops something novel. In the context of the current technologies and AI engines, this problem is particularly ripe as AI driven innovation will cause a radical shift in the pace, quality and areas of innovation which will need a massive overhaul of existing laws which do not allow non-human innovation to be registered in the current innovation market. To address this problem, we introduce a policy framework to adjudge innovation such that both human and machine driven innovation co-exists and complements each others R&D efforts. Our framework ensures that the legal status of machine is unchanged, the innovation quality surges and frivolous patent applications are pruned in early. The intention of this thesis is to elaborate, explore and mitigate the rough edges between AI and Law. The former part of the thesis proposes computational measures towards increased accountability, fairness and transparency in systems and the latter is directed towards a more policy angle between AI and Intellectual Property. The summum bonum of AI is to integrate with the human fabric and catalyze human efforts. This is possible if trust and acceptance is legally and socially established in these systems. This work is a small step towards the giant leap of making AI safe, governable and usable for the benefit of humanity. Full thesis: pdf Centre for Exact Humanities |
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