Safety Cases: A Scalable Approach to Frontier AI Safety

Fuente: arXiv
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Auteurs principaux: Hilton, Benjamin, Buhl, Marie Davidsen, Korbak, Tomek, Irving, Geoffrey
Format: Preprint
Publié: 2025
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author Hilton, Benjamin
Buhl, Marie Davidsen
Korbak, Tomek
Irving, Geoffrey
author_facet Hilton, Benjamin
Buhl, Marie Davidsen
Korbak, Tomek
Irving, Geoffrey
contents Safety cases - clear, assessable arguments for the safety of a system in a given context - are a widely-used technique across various industries for showing a decision-maker (e.g. boards, customers, third parties) that a system is safe. In this paper, we cover how and why frontier AI developers might also want to use safety cases. We then argue that writing and reviewing safety cases would substantially assist in the fulfilment of many of the Frontier AI Safety Commitments. Finally, we outline open research questions on the methodology, implementation, and technical details of safety cases.
format Preprint
id arxiv_https___arxiv_org_abs_2503_04744
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Safety Cases: A Scalable Approach to Frontier AI Safety
Hilton, Benjamin
Buhl, Marie Davidsen
Korbak, Tomek
Irving, Geoffrey
Computers and Society
Artificial Intelligence
Safety cases - clear, assessable arguments for the safety of a system in a given context - are a widely-used technique across various industries for showing a decision-maker (e.g. boards, customers, third parties) that a system is safe. In this paper, we cover how and why frontier AI developers might also want to use safety cases. We then argue that writing and reviewing safety cases would substantially assist in the fulfilment of many of the Frontier AI Safety Commitments. Finally, we outline open research questions on the methodology, implementation, and technical details of safety cases.
title Safety Cases: A Scalable Approach to Frontier AI Safety
topic Computers and Society
Artificial Intelligence
url https://arxiv.org/abs/2503.04744