Toward Effective AI Governance: A Review of Principles

TL;DR

This paper reviews existing AI governance frameworks, principles, and stakeholder roles, highlighting key directions and gaps in empirical validation and inclusivity to guide responsible AI development.

Contribution

It synthesizes current literature on AI governance, identifying prominent frameworks, principles, and gaps in actionable mechanisms and stakeholder strategies.

Findings

EU AI Act and NIST RMF are most cited frameworks

Transparency and accountability are the most common principles

Gaps exist in empirical validation and stakeholder inclusivity

Abstract

Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations.