Practice-Dependent Governance of AI

Abstract Recent advances in machine learning have made it feasible to deploy AI systems in many different contexts. When machines act without human input, they expose people to risks. Within business ethics, the dominant framework for addressing such risks has been the moral machine thesis: the idea that autonomous systems should emulate what is morally right in human interpersonal decision-making. Recently, it has been suggested that this approach should also guide regulation. Moral machine regulation is the view that the right regulatory approach to AI is to determine the correct choice in individual situations and craft regulation from these judgements. This paper argues that this view is mistaken. What is right in an individual case is not necessarily right when aggregated into a system of regulation, and what is right can depend on the regulatory framework itself. Instead, we argue for practice-dependent governance of AI. On this view, regulation of AI systems should aim to ensure that the practices in which they are deployed are just and justifiable to those affected, in light of the values and purposes those practices are meant to serve.