Toward Effective AI Governance: A Review of Principles
This work addresses the need for synthesis in AI governance literature to inform academic and practical efforts, but it is incremental as it reviews existing studies without proposing new methods or data.
The paper conducted a rapid tertiary review of nine secondary studies to identify key frameworks, principles, and gaps in AI governance literature, finding that the EU AI Act and NIST RMF are the most cited frameworks, with transparency and accountability as common principles, but noting a lack of actionable mechanisms and empirical validation.
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.