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AI explainability is the ability to explain, in human-understandable terms, why an AI system produced a particular output. It is distinct from interpretability (understanding the model's internal workings) and is a legal compliance requirement in multiple jurisdictions.
Key distinction: Explainability techniques explain specific decisions to specific people. Interpretability means understanding what a model computes internally. Both matter for governance, they are not interchangeable.
This page is general information about AI explainability, not legal, regulatory, or professional advice, and does not capture every nuance or exception. Requirements vary by jurisdiction and can be fact-specific. Always verify against primary sources and your own qualified legal counsel before relying on it.