# International Governance of Civilian AI: A Jurisdictional Certification Approach arXiv: https://arxiv.org/abs/2308.15514 ยท HTML: https://mlsanity.com/paper/2308.15514/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 134 references that are in the map. - First posted on arXiv 29 Aug 2023. ## Builds on (most cited first, 30 of 33) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022) - [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017) - [FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU](https://mlsanity.com/paper/2303.06865/) (2023) - [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023) - [Scaling Laws for Autoregressive Generative Modeling](https://mlsanity.com/paper/2010.14701/) (2020) - [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020) - [The Alignment Problem from a Deep Learning Perspective](https://mlsanity.com/paper/2209.00626/) (2022) - [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022) - [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023) - [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022) - [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023) - [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023) - [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023) - [Spear Phishing With Large Language Models](https://mlsanity.com/paper/2305.06972/) (2023) - [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022) - [The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination](https://mlsanity.com/paper/2304.14347/) (2023) - [The Role of Cooperation in Responsible AI Development](https://mlsanity.com/paper/1907.04534/) (2019) - [What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring](https://mlsanity.com/paper/2303.11341/) (2023) - [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022) - [Human or Not? A Gamified Approach to the Turing Test](https://mlsanity.com/paper/2305.20010/) (2023) - [Algorithmic progress in computer vision](https://mlsanity.com/paper/2212.05153/) (2022) - [International Institutions for Advanced AI](https://mlsanity.com/paper/2307.04699/) (2023) ## Built on by (most cited first, 8 of 8) - [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023) - [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024) - [Managing extreme AI risks amid rapid progress](https://mlsanity.com/paper/2310.17688/) (2023) - [Verification methods for international AI agreements](https://mlsanity.com/paper/2408.16074/) (2024) - [Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation](https://mlsanity.com/paper/2403.09510/) (2024) - [An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI](https://mlsanity.com/paper/2310.14455/) (2023) - [International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty](https://mlsanity.com/paper/2503.18956/) (2025) - [Governing dual-use technologies: Case studies of international security agreements and lessons for AI governance](https://mlsanity.com/paper/2409.02779/) (2024) ## Abstract (arXiv) This report describes trade-offs in the design of international governance arrangements for civilian artificial intelligence (AI) and presents one approach in detail. This approach represents the extension of a standards, licensing, and liability regime to the global level. We propose that states establish an International AI Organization (IAIO) to certify state jurisdictions (not firms or AI projects) for compliance with international oversight standards. States can give force to these international standards by adopting regulations prohibiting the import of goods whose supply chains embody AI from non-IAIO-certified jurisdictions. This borrows attributes from models of existing international organizations, such as the International Civilian Aviation Organization (ICAO), the International Maritime Organization (IMO), and the Financial Action Task Force (FATF). States can also adopt multilateral controls on the export of AI product inputs, such as specialized hardware, to non-certified jurisdictions. Indeed, both the import and export standards could be required for certification. As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.