Abstract: This paper is the introduction to the special issue entitled: Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance.

Abstract: Contrary to the criticism that mysterious, unaccountable black-box software systems threaten to make the logic of critical decisions inscrutable, we argue that algorithms are fundamentally understandable pieces of technology. Software systems are designed to interact with the world in a controlled way and built or operated for a specific purpose, subject to choices and assumptions. Traditional power structures can and do turn systems into opaque black boxes, but technologies can always be understood at a higher level, intensionally in terms of their designs and operational goals and extensionally in terms of their inputs, outputs and outcomes. The mechanisms of a systems operation can always be examined and explained, but a focus on machinery obscures the key issue of power dynamics. While structural inscrutability frustrates users and oversight entities, system creators and operators always determine that the technologies they deploy are fit for certain uses, making no system wholly inscrutable. We investigate the contours of inscrutability and opacity, the way they arise from power dynamics surrounding software systems, and the value of proposed remedies from disparate disciplines, especially computer ethics and privacy by design. We conclude that policy should not accede to the idea that some systems are of necessity inscrutable. Effective governance of algorithms comes from demanding rigorous science and engineering in system design, operation and evaluation to make systems verifiably trustworthy. Rather than seeking explanations for each behaviour of a computer system, policies should formalize and make known the assumptions, choices, and adequacy determinations associated with a system.

Abstract: Artificial intelligence (AI) is an emerging focus area of policy development in India. The countrys regional influence, burgeoning AI industry and ambitious governmental initiatives around AI make it an important jurisdiction to consider, regardless of where the reader of this article lives. Even as existing policy processes intend to encourage the rapid development of AI for economic growth and social good, an overarching trend persists in India, and several other jurisdictions: the limitations and risks of data-driven decisions still feature as retrospective considerations for development and deployment of AI applications. This article argues that the technical limitations of AI systems should be reckoned with at the time of developing policy, and the societal and ethical concerns that arise due to such limitations should be used to inform what policy processes aspire to achieve. It proposes a framework for such deliberation to occur, by analysing the three main stages of bringing machine learning (the most popular subset of AI techniques) to deployment–the data, model and application stage. It is written against the backdrop of Indias current AI policy landscape, and applies the proposed framework to ongoing sectoral challenges in India. With a view to influence existing policy deliberation in the country, it focuses on potential risks that arise from data-driven decisions in general, and in the Indian context in particular.