Safety cases for frontier AI

TL;DR

This paper explores the potential of safety cases as a structured method to demonstrate the safety of frontier AI systems, drawing parallels from other safety-critical industries and discussing implementation challenges.

Contribution

It introduces the concept of safety cases for frontier AI, explaining their potential role in governance and outlining practical steps for their development and integration.

Findings

Safety cases can enhance transparency and accountability in AI safety.

Implementing safety cases requires addressing practical challenges and establishing standards.

Safety cases have potential to inform regulatory and industry practices.

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.