TY - GEN
T1 - Two Means to an End Goal
T2 - 9th Annual ACM Conference on Fairness, Accountability, and Transparency, ACM FAccT 2026
AU - Schmude, Timothée
AU - Yurrita, Mireia
AU - Alfrink, Kars
AU - Le Goff, Thomas
AU - Tschiatschek, Sebastian
AU - Viard, Tiphaine
N1 - Publisher Copyright:
© 2026 Copyright held by the owner/author(s).
PY - 2026/6/25
Y1 - 2026/6/25
N2 - 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.
AB - 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.
KW - AI governance
KW - AI policy
KW - algorithmic accountability
KW - contestability
KW - explainability
KW - interdisciplinary collaboration
KW - public sector AI
KW - qualitative methods
KW - regulation-by-design
KW - transparency
KW - trustworthy AI
UR - https://www.scopus.com/pages/publications/105044418347
U2 - 10.1145/3805689.3812309
DO - 10.1145/3805689.3812309
M3 - Conference contribution
AN - SCOPUS:105044418347
T3 - ACM FAccT 2026 - Proceedings of the 9th annual ACM Conference on Fairness, Accountability, and Transparency
SP - 3078
EP - 3104
BT - ACM FAccT 2026 - Proceedings of the 9th annual ACM Conference on Fairness, Accountability, and Transparency
PB - Association for Computing Machinery, Inc
Y2 - 25 June 2026 through 28 June 2026
ER -