Explainability

Explainability is a responsible AI practice describing how and why a model arrived at a specific prediction, making outputs understandable to developers, auditors, and end users. It differs from interpretability, which concerns understanding a model’s overall mechanics; explainability focuses on justifying a particular output to a particular audience. Amazon SageMaker Clarify supports explainability through feature attribution, using SHAP values to quantify how much each input feature contributed to a prediction. On the AIF-C01 exam, watch for questions that conflate explainability with transparency: knowing a model’s architecture does not automatically make individual predictions explainable to a non-technical stakeholder.