Explainable AI: Making Model Decisions Accountable

A model that predicts well can still be impossible to justify, and "the algorithm decided" is not an answer to a rejected applicant, a clinician, or an auditor. This cursus builds the field from the mechanism up: the taxonomy and the global tools (permutation importance, partial dependence, ICE), then local attribution with LIME and SHAP including the Shapley axioms that make SHAP unique, then Integrated Gradients, Grad-CAM, and counterfactual explanations. It ends with the harder question: explanations can fail silently or be deliberately faked, so how do you check yours?