Toward Effective AI Governance: A Review of Principles

2025-05-29

AI Summary

Current AI governance research lacks systematic integration of diverse frameworks and practices, with notable gaps in the operationalizability of key mechanisms and the implementation of inclusive, stakeholder-centered approaches. To address this, we conduct a rapid three-tier literature review, systematically synthesizing nine authoritative IEEE/ACM reviews published between 2020 and 2024. We introduce the novel “thematic semantic synthesis” analytical paradigm to identify high-frequency governance frameworks (e.g., the EU AI Act, NIST AI Risk Management Framework), core principles (e.g., transparency, accountability), and stakeholder role distributions. Our analysis reveals four critical knowledge gaps in AI governance scholarship and practice. Based on these findings, we propose a rigorously grounded, organizationally feasible governance roadmap—bridging theoretical advancement and real-world implementation. This work contributes both empirical evidence and methodological innovation to advance AI governance research and practice.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Other Foundations of Human Computation & AI
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Consent frameworks and practices on the web
• Social Networks and Social Media: Generative AI / large language models and their impact on social 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.

Problem

• Synthesizing diverse AI governance frameworks and practices
• Identifying key principles like transparency and accountability
• Addressing gaps in empirical validation and inclusivity

Innovation

• Rapid tertiary review of secondary studies
• Thematic semantic synthesis for analysis
• Focus on EU AI Act and NIST RMF