Explainability

Explainability encompasses techniques and methods that make AI decision-making processes transparent and interpretable. This includes both intrinsically interpretable models (like decision trees) and post-hoc explanation methods (like LIME or SHAP). It involves breaking down complex model behaviors into understandable components, identifying feature importance, and providing counterfactual explanations.

Medical diagnosis systems explaining their reasoning to doctors, credit scoring systems justifying loan decisions, autonomous vehicle systems explaining driving decisions