Toward Effective AI Governance: A Review of Principles

--Background: 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: T o 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 (2020-2024), 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. 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. The increasing deployment of Artificial Intelligence (AI) systems across critical domains, such as healthcare, finance, public administration, has raised urgent concerns about their ethical, legal, and social implications [1]. In response, the notion of Responsible AI (RAI) has emerged as a multidimensional paradigm that seeks to ensure fairness, transparency, accountability, privacy, safety, and human oversight in the development and use of AI technologies [1].