Agents, Alignment, and the Many Faces of Autonomy

Abstract This article investigates the prospect of aligning AI agents with human autonomy. We show that doing so is a challenge: while there is general agreement on the concept’s broad contours, its components can be specified in a number of conflicting ways, making it difficult to decide how an autonomy-enhancing agent should act. In response, we introduce three strategies for dealing with disagreements about interpretations of autonomy. Each offers a coherent approach that captures a different set of considerations, with its own strength and trade-offs. The liberal approach is committed to user integrity and promotes human autonomy by acting strictly on what a person says they want, without trying to change their mind. The capability-boosting approach is built around the idea of flourishing and promotes human autonomy by empowering the user to act on their goals. The meta-autonomy approach prioritizes choice by giving the user the kind of autonomy they want in relation to their interactions with an AI agent. By highlighting the multifaceted nature of human autonomy, this article contributes to recent efforts seeking to identify and operationalize targets of AI value alignment. It also illustrates the challenges and trade-offs that can arise not just between—but even within—the values selected as targets for alignment and offers strategies to navigate them.