Skip to main content
AI-REGULATION-COMPLIANCE5 MIN READ

From Risk to Test Evidence

Convert an AI risk statement into a testable evidence plan with metric, segment, threshold, owner, and action rule.

The risk log says the support classifier may be unfair, but the team has no test that could prove the risk is controlled or force a launch change. Risk -> harm -> population -> metric -> segment -> threshold -> owner -> action rule. Common trap: use one average accuracy number and call it fairness evidence. Averages can improve while a relevant subgroup gets worse. Name the harm Define the concrete failure: urgent complaints from non-native English speakers may be misrouted to standard support. A test plan starts with a consequence, not a vague fairness word. Choose metric and segments…

Read the full lesson

Sign up free — one personalized lesson every day, matched to your role and goals.

Already have an account? Sign in

← Back to library
Contact us