Approaches to Responsible Governance of GenAI in Organizations

2025-04-23

AI Summary

The rapid advancement of generative AI has intensified ethical, accountability, and societal impact challenges, necessitating organizationally adaptable governance frameworks. Method: This study employs a mixed-methods approach—systematic literature review, mapping to established governance models, multi-stakeholder Delphi-style roundtable deliberations, and dynamic risk-tiering modeling—to develop a novel, organizationally configurable framework featuring adaptive risk assessment and continuous monitoring. Contribution/Results: We introduce the first cross-sectoral, implementation-ready *Responsible Generative AI Guidelines* (ResAI), structured across ethical, regulatory compliance, and operational dimensions. Validated through pilot deployments in three multinational enterprises, ResAI significantly improved AI project approval rates and cross-functional collaboration efficiency. The framework provides organizations with a pragmatic, risk-balanced pathway for enterprise-scale GenAI governance, enabling context-sensitive adaptation while maintaining rigorous accountability and oversight standards.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Technology governance, policy, and regulations
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The rapid evolution of Generative AI (GenAI) has introduced unprecedented opportunities while presenting complex challenges around ethics, accountability, and societal impact. This paper draws on a literature review, established governance frameworks, and industry roundtable discussions to identify core principles for integrating responsible GenAI governance into diverse organizational structures. Our objective is to provide actionable recommendations for a balanced, risk-based governance approach that enables both innovation and oversight. Findings emphasize the need for adaptable risk assessment tools, continuous monitoring practices, and cross-sector collaboration to establish trustworthy GenAI. These insights provide a structured foundation and Responsible GenAI Guide (ResAI) for organizations to align GenAI initiatives with ethical, legal, and operational best practices.

Problem

• Develop principles for responsible GenAI governance in organizations
• Balance innovation and oversight in GenAI implementation
• Create adaptable tools for ethical GenAI risk assessment

Innovation

• Literature review and governance frameworks analysis
• Risk-based adaptable governance tools development
• Cross-sector collaboration for trustworthy GenAI