An Adaptive Responsible AI Governance Framework for Decentralized Organizations

2025-10-03

AI Summary

Responsible Artificial Intelligence (RAI) governance faces significant implementation challenges in globally distributed organizations, primarily due to the limited applicability of existing frameworks under complex organizational structures and decentralized decision-making authority. Method: This study proposes ARGO—a novel, adaptive, three-tier RAI governance framework that integrates centralized coordination with local autonomy and supports modular, context-sensitive deployment. Its design and efficacy were rigorously evaluated through a multi-case, cross-departmental assessment spanning diverse business units and AI application domains. Contribution/Results: The evaluation identified four recurrent barrier patterns impeding RAI implementation. Empirical findings demonstrate that ARGO significantly enhances accountability mechanisms, standardization consistency, and local adaptability. By reconciling global coherence with contextual flexibility, ARGO establishes a new governance paradigm for decentralized organizations seeking scalable, operationally viable RAI oversight.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: AI for Accessibility

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Assessing Responsible AI governance in decentralized organizations
• Translating abstract AI principles into operational practices
• Balancing central coordination with local autonomy

Innovation

• Adaptive framework balancing central coordination with local autonomy
• Three-layer structure combining standards, advisory resources, and implementation
• Modular governance approach accommodating organizational complexity