Safety cases for frontier AI

TL;DR

Frontier AI carries high-stakes safety risks that are not fully addressed by existing framework-level policies. The paper assesses safety cases—structured, evidence-backed arguments about safety—and explains how they can complement safety frameworks for frontier AI, including how they could be implemented in industry and regulation. It details the four core components (scope, objectives, arguments, evidence), sketches what frontier AI-specific safety cases might look like, and analyzes practical challenges in methodology, safeguards, and governance structures. The authors argue that safety cases are valuable now but require coordinated research, capacity-building, and an active third-party ecosystem to mature into a reliable decision-support tool for deploying and regulating frontier AI.

Abstract

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.

Figures (6)

• Figure 1: Process for using a safety case to inform a decision
• Figure 2: Sketch of a safety case argument
• Figure 3: Sketch of an internal safety case review process
• Figure 4: The stage and role of safety cases at each stage of the system lifecycle
• Figure 5: Definition of a safety case argument