Calibrating evaluation rigor for high-stakes AI applications

Determine principled criteria and methods for the required level of rigor and confidence in AI model evaluations for decision-making systems in high‑stakes settings, aligning evaluation strength with use-case risk.

Background

AI evaluations in high-stakes contexts (e.g., healthcare, finance, safety-critical operations) demand confidence beyond typical applications, but current practice lacks clear guidance for how rigorous evaluations should be.

Establishing risk-aligned evaluation rigor would improve the reliability of deployed systems and help regulators and practitioners select appropriate testing protocols.