Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers
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Abstract (arXiv)
To address security and safety risks stemming from highly capable artificial intelligence (AI) models, we propose that the US government should ensure compute providers implement Know-Your-Customer (KYC) schemes. Compute - the computational power and infrastructure required to train and run these AI models - is emerging as a node for oversight. KYC, a standard developed by the banking sector to identify and verify client identity, could provide a mechanism for greater public oversight of frontier AI development and close loopholes in existing export controls. Such a scheme has the potential to identify and warn stakeholders of potentially problematic and/or sudden advancements in AI capabilities, build government capacity for AI regulation, and allow for the development and implementation of more nuanced and targeted export controls. Unlike the strategy of limiting access to AI chip purchases, regulating the digital access to compute offers more precise controls, allowing regulatory control over compute quantities, as well as the flexibility to suspend access at any time. To enact a KYC scheme, the US government will need to work closely with industry to (1) establish a dynamic threshold of compute that effectively captures high-risk frontier model development, while minimizing imposition on developers not engaged in frontier AI; (2) set requirements and guidance for compute providers to keep records and report high-risk entities; (3) establish government capacity that allows for co-design, implementation, administration and enforcement of the scheme; and (4) engage internationally to promote international alignment with the scheme and support its long-term efficacy. While the scheme will not address all AI risks, it complements proposed solutions by allowing for a more precise and flexible approach to controlling the development of frontier AI models and unwanted AI proliferation.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2023.
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Citing papers per year: 2023: 1; 2024: 5; 2025: 4; 2026: 2.
12 papers.
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- 2026Bounded Sovereignty and the Control Tax: Pricing AI Oversight When the Deployer Does Not Own the Modelcited by 0
- 2026Bounded Sovereignty and the Control Tax: Pricing AI Oversight When the Deployer Does Not Own the Model6 Jul 2026
- 2026Know Your Scientist: KYC as Biosecurity Infrastructure5 Feb 2026
- 2025Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape10 Jul 2025
- 2025Misalignment or misuse? The AGI alignment tradeoff4 Jun 2025
- 2025International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty18 Mar 2025
- 2025Authenticated Delegation and Authorized AI Agents16 Jan 2025
- 2024IDs for AI Systems17 Jun 2024
- 2024Training Compute Thresholds: Features and Functions in AI Regulation17 May 2024
- 2024Responsible Reporting for Frontier AI Development3 Apr 2024
- 2024Combatting deepfakes: Policies to address national security threats and rights violations14 Feb 2024
- 2024Visibility into AI Agents23 Jan 2024
- 2023AI capabilities can be significantly improved without expensive retraining12 Dec 2023