# International Institutions for Advanced AI

arXiv: https://arxiv.org/abs/2307.04699 · HTML: https://mlsanity.com/paper/2307.04699/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2023.

## Built on by (most cited first, 9 of 9)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [Managing extreme AI risks amid rapid progress](https://mlsanity.com/paper/2310.17688/) (2023)
- [Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation](https://mlsanity.com/paper/2403.09510/) (2024)
- [International Governance of Civilian AI: A Jurisdictional Certification Approach](https://mlsanity.com/paper/2308.15514/) (2023)
- [Multinational AGI Consortium (MAGIC): A Proposal for International Coordination on AI](https://mlsanity.com/paper/2310.09217/) (2023)
- [Contextual Confidence and Generative AI](https://mlsanity.com/paper/2311.01193/) (2023)
- [The Normalization of Deviance in AI Development](https://mlsanity.com/paper/2609.05749/) (2026)
- [Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance](https://mlsanity.com/paper/2609.10105/) (2026)

## Abstract (arXiv)

International institutions may have an important role to play in ensuring advanced AI systems benefit humanity. International collaborations can unlock AI's ability to further sustainable development, and coordination of regulatory efforts can reduce obstacles to innovation and the spread of benefits. Conversely, the potential dangerous capabilities of powerful and general-purpose AI systems create global externalities in their development and deployment, and international efforts to further responsible AI practices could help manage the risks they pose. This paper identifies a set of governance functions that could be performed at an international level to address these challenges, ranging from supporting access to frontier AI systems to setting international safety standards. It groups these functions into four institutional models that exhibit internal synergies and have precedents in existing organizations: 1) a Commission on Frontier AI that facilitates expert consensus on opportunities and risks from advanced AI, 2) an Advanced AI Governance Organization that sets international standards to manage global threats from advanced models, supports their implementation, and possibly monitors compliance with a future governance regime, 3) a Frontier AI Collaborative that promotes access to cutting-edge AI, and 4) an AI Safety Project that brings together leading researchers and engineers to further AI safety research. We explore the utility of these models and identify open questions about their viability.