This report was a collaboration between the Martin Oxford School, Centre for the Governance of AI, Institute for Law and AI, Stanford Intelligent Systems Laboratory, Safer AI, and Centre for Future Generations.

As artificial intelligence advances rapidly, the question of international governance remains unresolved. While global cooperation is often complex and costly, not every AI issue requires cross-border coordination and unified governance. Therefore, how international actors should prioritize their collaborative efforts represents a critical challenge in AI governance.

This report explores how, and to what extent, internationalisation can be pursued to create effective AI governance that facilitates the adoption of AI innovations without jeopardizing their innovative potential. In particular, four key factors determine which AI issues require international cooperation:

1. Cross-border externalities that transcend national boundaries
2. Regulatory arbitrage risks that undermine competitive positioning
3. Uneven governance capacities that create systemic vulnerabilities
4. Interoperability requirements essential for technological leadership

In this paper, our analysis reveals compelling advantages for international coordination in 5 policy areas such as compute-provider oversight, content provenance, model evaluations, incident monitoring, and risk management protocols. In these domains, stronger coordination at the international level, with a focus on AI governance as well as the adoption and diffusion of both AI technologies and governance frameworks, should be prioritized because of the positive effects it may have.

In contrast, the benefits of internationalisation appear lower or mixed in fields such as data privacy, data provenance, chip distribution, and bias mitigation. Hence, in these areas, governance should be developed either at the national level or, alternatively, through less internationalisation. 

This report was a collaboration between the Martin Oxford School, Centre for the Governance of AI, Institute for Law and AI, Stanford Intelligent Systems Laboratory, Safer AI, and Centre for Future Generations.

As artificial intelligence advances rapidly, the question of international governance remains unresolved. While global cooperation is often complex and costly, not every AI issue requires cross-border coordination and unified governance. Therefore, how international actors should prioritize their collaborative efforts represents a critical challenge in AI governance.

This report explores how, and to what extent, internationalisation can be pursued to create effective AI governance that facilitates the adoption of AI innovations without jeopardizing their innovative potential. In particular, four key factors determine which AI issues require international cooperation:

1. Cross-border externalities that transcend national boundaries
2. Regulatory arbitrage risks that undermine competitive positioning
3. Uneven governance capacities that create systemic vulnerabilities
4. Interoperability requirements essential for technological leadership

In this paper, our analysis reveals compelling advantages for international coordination in 5 policy areas such as compute-provider oversight, content provenance, model evaluations, incident monitoring, and risk management protocols. In these domains, stronger coordination at the international level, with a focus on AI governance as well as the adoption and diffusion of both AI technologies and governance frameworks, should be prioritized because of the positive effects it may have.

In contrast, the benefits of internationalisation appear lower or mixed in fields such as data privacy, data provenance, chip distribution, and bias mitigation. Hence, in these areas, governance should be developed either at the national level or, alternatively, through less internationalisation.