An Adaptive Responsible AI Governance Framework for Decentralized Organizations

This paper examines the assessment challenges of Respon- sible AI (RAI) governance efforts in globally decentralized organizations through a case study collaboration between a leading research university and a multinational enterprise. While there are many proposed frameworks for RAI, their ap- plication in complex organizational settings with distributed decision-making authority remains underexplored. Our RAI assessment, conducted across multiple business units and AI use cases, reveals four key patterns that shape RAI imple- mentation: (1) complex interplay between group-level guid- ance and local interpretation, (2) challenges translating ab- stract principles into operational practices, (3) regional and functional variation in implementation approaches, and (4) inconsistent accountability in risk oversight. Based on these findings, we propose an Adaptive RAI Governance (ARGO) Framework that balances central coordination with local au- tonomy through three interdependent layers: shared founda- tion standards, central advisory resources, and contextual lo- cal implementation. We contribute insights from academic- industry collaboration for RAI assessments, highlighting the importance of modular governance approaches that accom- modate organizational complexity while maintaining align- ment with responsible AI principles. These lessons offer prac- tical guidance for organizations navigating the transition from RAI principles to operational practice within decentralized structures.

Authors: Kiana Jafari Meimandi, Anka Reuel, Gabriela Aranguiz-Dias, Hatim Rahama, Ala-Eddine Ayadi, Xavier Boullier, Jérémy Verdo, Louis Montanie, Mykel Kochenderfer

Published in: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Publication date: 2025-10-15

Read the paper: https://doi.org/10.1609/aies.v8i2.36633

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