Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI

Explainability and its emerging counterpart contestability are key normative and design principles for trustworthy AI, enabling users and subjects to understand and challenge AI decisions. Yet realizing these principles is difficult, as they take on different meanings across technical, legal, and organizational dimensions of AI regulation. To address this conceptual polysemy, we report findings from an interview study with 14 experts examining the intersection and implementation of explainability and contestability, and their interpretations in different research communities. We outline differentiations between descriptive and normative explainability, judicial and non-judicial channels of contestation, and individual and collective contestation action. We also identify key points of friction in realizing both principles, including alignment between top-down and bottom-up regulation, assignment of responsibility, and the need for interdisciplinary collaboration. Finally, we offer three AI policy recommendations to operationalize explainability and contestability through a Regulation-by-Design perspective. Our contributions inform policy research and regulation of these core principles, and support more effective and equitable design, development, and deployment of trustworthy public AI systems.