用可评估的安全论证框架，提升前沿AI系统的可信度与合规性。
Safety Cases: A Scalable Approach to Frontier AI Safety
- 构建清晰可验证的安全论证链条，支持决策者判断系统安全性
- 能有效支撑前沿AI安全承诺的落实与追踪
- 适合政策制定者、开发者及第三方审核方参考使用
安全案例——在特定情境下对系统安全性给出清晰、可评估的论据——是多个行业广泛采用的技术，用于向决策者（如董事会、客户、第三方）证明系统的安全性。本文探讨前沿AI开发者为何应采纳安全案例，并论证其在实现多项前沿AI安全承诺方面具有显著助益。同时，文章提出安全案例在方法论、实施路径和技术细节方面的开放性研究问题，为未来实践提供方向。
原文摘要 · Abstract (English)
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.
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