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All glossary terms

AI governance

AI Explainability

AI explainability is the ability to provide useful reasons or evidence about how an artificial intelligence system produced an output. What counts as useful depends on the audience and decision; an explanation may describe inputs, model behavior, or a local rationale without revealing the model’s complete internal computation.

Also known as explainable AI.

Why it matters in practice

Explanations can support debugging, oversight, communication, and contesting consequential outcomes, but a fluent explanation is not automatically faithful to a model’s operation. Teams should distinguish measured evidence from a generated narrative and test whether the explanation serves its intended audience. Explainability alone does not establish accuracy, fairness, or legal sufficiency.

Example

An analyst compares a feature-attribution report with the model’s recorded inputs before explaining why a particular classification was produced.

Educational note

This definition is general information, not legal advice. It may omit facts that matter to a specific situation and does not establish or guarantee compliance.