Artificial Intelligence Governance: A Survey

Summary

This comprehensive academic survey cuts through the fragmented landscape of AI governance to deliver a unified analysis of regulatory approaches worldwide. Rather than focusing on a single jurisdiction or framework, the authors systematically examine governance models across multiple regions, comparing their effectiveness and identifying emerging patterns in how societies are attempting to govern AI systems. For anyone trying to make sense of the rapidly evolving patchwork of AI regulations, this paper serves as an essential roadmap that connects the dots between different approaches and reveals which strategies are actually working in practice.

What makes this different

Unlike most governance papers that advocate for a particular approach, this survey takes a deliberately analytical stance, examining what's actually happening in AI governance rather than what should happen. The authors don't just catalog existing regulations—they evaluate their effectiveness, identify gaps, and analyze how different governance mechanisms interact with each other. This meta-analysis approach is particularly valuable given how quickly the governance landscape is shifting, with new regulations and frameworks appearing monthly across different jurisdictions.

Key findings and insights

The survey reveals several critical patterns in how AI governance is evolving:

• Regulatory convergence is happening faster than expected. Despite different starting points, jurisdictions are gravitating toward similar core principles around transparency, accountability, and risk assessment, suggesting some natural evolution toward common standards.
• Sector-specific governance is outpacing general AI laws. While comprehensive AI acts grab headlines, the most effective governance is often happening through targeted regulations in finance, healthcare, and transportation that address AI's specific risks in those domains.
• Self-regulatory mechanisms are proving insufficient alone. The paper documents how voluntary industry initiatives, while valuable for setting technical standards, consistently fail to address broader societal impacts without regulatory backing.
• Enforcement remains the weakest link. Even well-designed governance frameworks struggle with the practical challenges of monitoring AI systems and ensuring compliance across complex, rapidly evolving technology stacks.

Who this resource is for

• Policy researchers and academics will find this an invaluable starting point for understanding the current state of AI governance scholarship and identifying research gaps. The comprehensive literature review saves months of groundwork.
• Government officials and regulators can use this analysis to learn from other jurisdictions' experiences and avoid repeating common mistakes in governance design. The comparative framework is particularly useful for crafting new legislation.
• Corporate governance and compliance professionals need this perspective to understand how different regulatory approaches might affect their organizations and to anticipate where governance requirements are heading.
• Civil society organizations and advocacy groups will benefit from the paper's analysis of governance effectiveness, helping them focus their efforts on approaches most likely to achieve meaningful impact.

The academic context

This survey emerges at a critical inflection point in AI governance, published just as major regulations like the EU AI Act are moving from design to implementation. The timing allows the authors to analyze not just regulatory intent but early evidence of real-world impact. The paper builds on the growing body of "governance of emerging technologies" scholarship but applies it specifically to AI's unique challenges around opacity, scale, and societal impact.

The methodology combines systematic literature review with comparative policy analysis, drawing from legal scholarship, public policy research, and technology studies. This interdisciplinary approach reflects the reality that effective AI governance requires expertise across multiple domains.

Limitations to keep in mind

As with any survey of a fast-moving field, some analysis may become outdated quickly as new regulations take effect and governance approaches evolve. The paper also necessarily focuses on formal governance mechanisms, potentially underweighting informal industry practices and emerging governance innovations.

The academic perspective, while valuable for analytical rigor, may sometimes miss practical implementation challenges that become apparent only when regulations meet real-world deployment scenarios. The global scope, though comprehensive, means less depth on any single jurisdiction's specific context and constraints.