LawAI Publishes Literature Review Mapping AI Governance Problems, Policy Options, and Research Gaps

LawAI published the Advanced AI Governance: A Literature Review of Problems, Options and Research Challenges in January 2025, providing a structured survey of the academic and policy literature on frontier AI governance mechanisms. The review covers a broad set of regulatory instruments including compute security measures, software and hardware export controls, licensing regimes for advanced AI systems, structured system evaluations, and procurement rules designed to advance AI safety objectives. It also examines voluntary corporate governance proposals such as Responsible Scaling Policies, which several leading AI developers have adopted, and formal AI certification schemes being explored by standards bodies and regulators. The document does not make binding recommendations but synthesizes existing research to identify where evidence is strong, where significant gaps remain, and which governance questions require further empirical or legal investigation.

The review reflects a broader trend in AI governance research toward systematizing and comparing the range of available policy instruments rather than advocating for any single approach. Regulatory bodies at the international level, including the OECD and relevant UN agencies, have increasingly called for evidence-based governance frameworks, and academic institutions such as LawAI are responding by mapping what the literature does and does not yet support. The inclusion of compute governance and export controls is particularly timely given active policy discussions in the United States, the United Kingdom, and the European Union around controlling access to advanced semiconductor hardware and related software. The review's treatment of system evaluations also connects directly to the EU AI Act's requirements for conformity assessments and third-party auditing of high-risk and general-purpose AI models, as well as to NIST's AI Risk Management Framework, which similarly emphasizes structured testing and evaluation as core compliance activities.

Compliance and risk teams at enterprises developing or deploying advanced AI systems should treat this literature review as a reference document for anticipating regulatory direction rather than responding to existing mandates. Teams responsible for AI governance strategy should pay particular attention to the sections on licensing and certification schemes, as these mechanisms are under active consideration in multiple jurisdictions and could impose new pre-market approval obligations on developers of frontier models. Procurement officers and legal counsel should also note the review's treatment of AI procurement rules, which signals growing government interest in using purchasing power to enforce safety and transparency standards on vendors. Organizations that have not yet assessed their exposure to compute-related export controls should assign that analysis to their trade compliance teams promptly, given the pace at which hardware export restrictions have been tightened in the United States and mirrored by allied governments. Finally, the review's mapping of open research questions can help internal policy and public affairs teams identify where engagement with regulators and standards bodies may be most productive during the current period of active framework development.