Resolving value conflicts in public AI governance: A procedural justice framework

This paper addresses the challenge of resolving value conflicts in the public governance of artificial intelligence (AI). While existing AI ethics and regulatory frameworks emphasize a range of normative criteria—such as accuracy, transparency, fairness, and accountability—many of these values are in tension and, in some cases, incommensurable. I propose a procedural justice framework that distinguishes between conflicts among derivative trustworthiness criteria and those involving fundamental democratic values. For the former, I apply analytical tools such as the Dominance Principle, Supervaluationism, and Maximality to eliminate clearly inferior alternatives. For the latter, I argue that justifiable decision-making requires procedurally fair deliberation grounded in widely endorsed principles such as publicity, inclusion, relevance, and appeal. I demonstrate the applicability of this framework through an indepth analysis of an AI-based decision support system used by the Swedish Public Employment Service (PES), showing how institutional decision-makers can navigate complex trade-offs between efficiency, explainability, and legality. The framework provides public institutions with a structured method for addressing normative conflicts in AI implementation, moving beyond technical optimization toward democratically legitimate governance.