Explainable AI is necessary, but it’s not enough

The text argues that mainstream explainability methods focus on explaining a model's reasoning after inference, leaving a blind spot: decisions also depend on the correctness and provenance of knowledge and policy inputs. For a decision to be fully explainable, reasoning, knowledge, and policy must all be explainable and verifiable together—explainable AI currently addresses only the reasoning component and thus is necessary but insufficient.