Explainable AI (XAI)
Also called: XAI
Explainable AI (XAI) is the set of methods and engineering techniques that make the internal decision-making pathways and outputs of complex machine learning models understandable to human domain experts. It produces interpretable summaries, feature importance rankings and decision trees, so risk managers and regulators can verify that a model is safe, diagnose why it failed and evidence compliance.
In practice
In credit underwriting, insurance and medical diagnostics, a model you cannot explain is a model you cannot deploy, because a regulator will eventually ask why a particular decision was reached. Budget for explainability during the build rather than as a retrofit: adding it after a black-box model is already in production is the expensive route.