Reliable assurance for future frontier AI systems with dangerous capabilities

Develop reliable assurance methodology and safeguards that enable the construction of adequate safety cases for frontier AI systems possessing significantly dangerous capabilities, ensuring such systems do not pose unacceptable risk in their deployment context.

Background

The paper highlights that as frontier AI systems acquire more dangerous capabilities, the current state of practice lacks reliable methods to assure the safety of such systems. This gap directly impedes the ability to produce adequate safety cases for future deployments. The authors emphasize that significant research into novel safety techniques is necessary to make safety cases viable for high-capability systems.