# Toward Effective AI Governance: A Review of Principles

arXiv: https://arxiv.org/abs/2505.23417 · HTML: https://mlsanity.com/paper/2505.23417/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 7 of 7)

- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Typology of Risks of Generative Text-to-Image Models](https://mlsanity.com/paper/2307.05543/) (2023)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability](https://mlsanity.com/paper/2311.13158/) (2023)
- [Oil & Water? Diffusion of AI Within and Across Scientific Fields](https://mlsanity.com/paper/2405.15828/) (2024)
- [Rapid Reviews in Software Engineering](https://mlsanity.com/paper/2003.10006/) (2020)
- [Towards a Privacy and Security-Aware Framework for Ethical AI: Guiding the Development and Assessment of AI Systems](https://mlsanity.com/paper/2403.08624/) (2024)

## Built on by (most cited first, 2 of 2)

- [Towards Selection as Power: Bounding Decision Authority in Autonomous Agents](https://mlsanity.com/paper/2602.14606/) (2026)
- [Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws](https://mlsanity.com/paper/2608.14568/) (2026)

## Abstract (arXiv)

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.