Toward Effective AI Governance: A Review of Principles

TL;DR

The paper conducts a rapid tertiary review of secondary AI governance literature from 2020–2024 to map frameworks, principles, mechanisms, and stakeholder roles. It identifies dominant frameworks (e.g., EU AI Act, NIST RMF) and core principles (transparency, accountability) while highlighting gaps in actionable governance mechanisms and inclusive stakeholder engagement. The analysis provides an integrative overview for researchers and practitioners and calls for empirical validation of governance practices in real-world settings. Overall, the work clarifies the state of AI governance discourse and points to concrete, organization-level practices that could advance responsible deployment of AI systems.

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.