Quantitative or qualitative measures (e.g., feature importance scores, explanation fidelity) used to assess the quality and reliability of AI explanations.
Metrics that evaluate explanation properties such as fidelity (how well explanations match the true model behavior), stability (consistency of explanations under similar inputs), comprehensiveness (coverage of key features), and simplicity (conciseness for users). Governance uses these metrics to benchmark explanation methods, set acceptance thresholds, and track improvement over time.
An e-commerce platform’s fraud-alert explanations are scored for fidelity by measuring the correlation between feature-attribution rankings and actual model sensitivity. Only explanation methods with fidelity > 0.85 are approved for end-user dashboards, ensuring reliable insights for investigators.
