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Safety cases for frontier AI

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arxiv 2410.21572 v1 pith:AWEDBIHF submitted 2024-10-28 cs.CY

Marie Davidsen Buhl , Gaurav Sett , Leonie Koessler , Jonas Schuett , Markus Anderljung This is my paper
classification cs.CY
keywords safetycasesfrontierexplainsafesystemsalreadyargument
verification ladder T0 review T1 audit T2 compute T3 formal
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As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do so is via safety cases: reports that make a structured argument, supported by evidence, that a system is safe enough in a given operational context. Safety cases are already common in other safety-critical industries such as aviation and nuclear power. In this paper, we explain why they may also be a useful tool in frontier AI governance, both in industry self-regulation and government regulation. We then discuss the practicalities of safety cases, outlining how to produce a frontier AI safety case and discussing what still needs to happen before safety cases can substantially inform decisions.

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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

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    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.

  2. Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework

    cs.AI 2025-07 conditional novelty 6.0 of 10

    A framework paper that adapts AI safety case methodology to the specific threat of manipulation attacks by internally deployed misaligned AI.

  3. Levels of Autonomy for AI Agents

    cs.HC 2025-06 conditional novelty 6.0 of 10

    A user-role-based five-level framework for designing, certifying, and evaluating AI agent autonomy as a choice independent of agent capability.

  4. An Example Safety Case for Safeguards Against Misuse

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A proposed framework, built around an 'uplift model' that translates red-team safeguard-evasion data into estimated risk, for justifying that AI misuse safeguards keep large-scale harm risk below a threshold.

  5. Systematic Hazard Analysis for Frontier AI using STPA

    cs.CY 2025-06 conditional novelty 5.0 of 10

    Applying STPA to the AI Control scenario produces structured unsafe control actions and loss scenarios, supporting an argument that systematic hazard analysis can improve frontier AI safety assurance.

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