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Verification methods for international AI agreements

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arxiv 2408.16074 v2 pith:FK7L5ALD submitted 2024-08-28 cs.CY cs.AI

Akash R. Wasil , Tom Reed , Jack William Miller , Peter Barnett This is my paper · ORCID
classification cs.CYcs.AI
keywords methodsverificationagreementsinternationalunauthorizedadvancedrequiresuspected
verification ladder T0 review T1 audit T2 compute T3 formal
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What techniques can be used to verify compliance with international agreements about advanced AI development? In this paper, we examine 10 verification methods that could detect two types of potential violations: unauthorized AI training (e.g., training runs above a certain FLOP threshold) and unauthorized data centers. We divide the verification methods into three categories: (a) national technical means (methods requiring minimal or no access from suspected non-compliant nations), (b) access-dependent methods (methods that require approval from the nation suspected of unauthorized activities), and (c) hardware-dependent methods (methods that require rules around advanced hardware). For each verification method, we provide a description, historical precedents, and possible evasion techniques. We conclude by offering recommendations for future work related to the verification and enforcement of international AI governance agreements.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements

    cs.CY 2026-06 conditional novelty 6.0 of 10

    Verification of international AI agreements will fail first at detecting hidden compute facilities, around the 10,000-H100-equivalent scale, before other enforcement mechanisms break.

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  3. Safety Features for a Centralised AGI Project

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  4. Toward a Global Regime for Compute Governance: Building the Pause Button

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    The paper argues that a global, enforceable compute pause is achievable through a layered framework of hardware controls, supply chain tracking, and regulation.

  5. Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation

    cs.CR 2025-07 reject novelty 2.0 of 10

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