  As AI systems become more advanced, companies and regulators will make
difficult decisions about whether it is safe to train and deploy them. To
prepare for these decisions, we investigate how developers could make a 'safety
case,' which is a structured rationale that AI systems are unlikely to cause a
catastrophe. We propose a framework for organizing a safety case and discuss
four categories of arguments to justify safety: total inability to cause a
catastrophe, sufficiently strong control measures, trustworthiness despite
capability to cause harm, and -- if AI systems become much more powerful --
deference to credible AI advisors. We evaluate concrete examples of arguments
in each category and outline how arguments could be combined to justify that AI
systems are safe to deploy.
