Model Explainability Review overview

Reviews the interpretability and explainability posture of ML models before deployment — generating SHAP/LIME feature importance explanations, validating that global and local explanations are consistent, verifying explanation latency meets serving SLAs, documenting known blind spots and out-of-distribution behavior, and ensuring explanations are surfaced appropriately in end-user interfaces. Excludes model training and fairness auditing.