An AI safety case is a structured argument that a specific AI system is acceptably safe for a defined use, supported by evidence and explicit assumptions. It links hazards to controls, tests, monitoring, and residual risk, and must be updated when the system, users, environment, or evidence changes.
The case starts with a bounded claim, identifies hazards, and links each control to evidence. AI agent evals and incident exercises support it, while AI assurance reviews the argument. A safety case is an evidence map, not a certificate of perfect safety.
When does AI safety case matter?
Use it for consequential deployments where decision makers need a reviewable basis for approval. Connect it to AI risk management and agent incident response. Assumptions must stay visible because a changed context can invalidate the claim.
Frequently asked questions
What is AI safety case used for?
Use it for consequential deployments where decision makers need a reviewable basis for approval. Connect it to AI risk management and agent incident response.
Who approves an AI safety case?
The accountable organization decides, often with independent review, according to the system's risk and regulatory context.
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