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An Adaptive Responsible AI Governance Framework for Decentralized Organizations

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

TL;DR

The paper tackles the challenge of translating Responsible AI principles into practice within globally decentralized organizations. Using a university–industry case study across 50+ autonomous business units, it analyzes governance, implementation practices, and accountability, and proposes the Adaptive RAI Governance (ARGO) framework. ARGO introduces a three-layer, modular structure—Shared Foundation, Advisory Resources, and Local Implementation—to balance central coordination with local autonomy, informed by four recurring implementation patterns: group guidance versus local interpretation, principle-to-practice translation, regional variation, and accountability gaps. The work yields practical recommendations and a path toward standardization in practice, with implications for cross-unit benchmarking and ongoing academia–industry collaboration in RAI governance.

Abstract

This paper examines the assessment challenges of Responsible 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 application 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 implementation: (1) complex interplay between group-level guidance and local interpretation, (2) challenges translating abstract 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 autonomy through three interdependent layers: shared foundation standards, central advisory resources, and contextual local implementation. We contribute insights from academic-industry collaboration for RAI assessments, highlighting the importance of modular governance approaches that accommodate organizational complexity while maintaining alignment with responsible AI principles. These lessons offer practical guidance for organizations navigating the transition from RAI principles to operational practice within decentralized structures.

An Adaptive Responsible AI Governance Framework for Decentralized Organizations

TL;DR

The paper tackles the challenge of translating Responsible AI principles into practice within globally decentralized organizations. Using a university–industry case study across 50+ autonomous business units, it analyzes governance, implementation practices, and accountability, and proposes the Adaptive RAI Governance (ARGO) framework. ARGO introduces a three-layer, modular structure—Shared Foundation, Advisory Resources, and Local Implementation—to balance central coordination with local autonomy, informed by four recurring implementation patterns: group guidance versus local interpretation, principle-to-practice translation, regional variation, and accountability gaps. The work yields practical recommendations and a path toward standardization in practice, with implications for cross-unit benchmarking and ongoing academia–industry collaboration in RAI governance.

Abstract

This paper examines the assessment challenges of Responsible 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 application 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 implementation: (1) complex interplay between group-level guidance and local interpretation, (2) challenges translating abstract 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 autonomy through three interdependent layers: shared foundation standards, central advisory resources, and contextual local implementation. We contribute insights from academic-industry collaboration for RAI assessments, highlighting the importance of modular governance approaches that accommodate organizational complexity while maintaining alignment with responsible AI principles. These lessons offer practical guidance for organizations navigating the transition from RAI principles to operational practice within decentralized structures.

Paper Structure

This paper contains 37 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Governance and Accountability
  4. Translating Principles to Practice Across Contexts
  5. Balancing Local Autonomy with Coordination
  6. Managing Cross-Jurisdictional Regulatory Variation
  7. RAI Implementation Gap
  8. Academic–Industry Collaboration on RAI
  9. RAI Assessment Context
  10. Decentralized Global Enterprise
  11. Collaboration Scope and Structure
  12. Scope of the Assessment
  13. Methodology
  14. Data Collection Methods
  15. Analysis Process
  16. ...and 22 more sections

Figures (2)

  • Figure 1: Responsible AI assessment process.
  • Figure 2: Adaptive RAI Governance (ARGO) Framework.