Definition · Plain-language
AI explainability
AI explainability (XAI) is the degree to which an AI system’s outputs and behaviour can be understood and explained in human terms.
The step most authors miss
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Explainability versus interpretability
The two terms are related but not identical. Interpretability usually refers to how far a model’s internal mechanics can be understood directly — a small decision tree or linear model is inherently interpretable because a person can follow its logic. Explainability is broader: it is the ability to produce meaningful, human-understandable accounts of why a system behaved as it did, even when the model itself is complex and opaque. For modern deep-learning systems that are not directly interpretable, explainability is often achieved through post-hoc techniques that approximate or illuminate the reasons behind an output rather than exposing the full internal computation.
Why explainability matters
Explainability serves several governance goals at once. It enables meaningful human oversight, because a reviewer cannot sensibly approve or override a decision they cannot understand. It supports contestability, letting an affected person challenge an outcome and seek redress. It aids debugging and assurance, helping developers and auditors detect bias, spurious correlations or failure modes. And it builds justified trust, distinguishing systems that are reliable for the right reasons from those that happen to perform well by accident. For these reasons the NIST AI RMF lists explainability and interpretability among the characteristics of trustworthy AI.
Approaches and trade-offs
Explainability can be pursued by using inherently interpretable models where the task allows, or by applying post-hoc explanation methods to complex models — for example identifying which inputs most influenced an output, or generating example-based or rule-based approximations. Each approach has limits: simpler models may trade away accuracy, while post-hoc explanations are approximations that can mislead if treated as the model’s true reasoning. There is often a tension between predictive performance and ease of explanation. Good practice matches the level and form of explanation to the audience and the stakes, ensuring explanations are faithful and genuinely useful rather than reassuring but inaccurate.
Key facts
At a glance
- Definition: the degree to which an AI system’s outputs can be understood and explained by humans
- Abbreviation: XAI (explainable AI)
- Interpretability: how far a model’s internal workings are directly understandable
- Purpose: enable oversight, contestability, debugging and justified trust
- Standards link: a trustworthiness characteristic in the NIST AI RMF
- Trade-off: predictive performance versus ease of explanation
Common misconceptions
What people often get wrong
Often heard: Explainability and interpretability mean exactly the same thing.
Actually: Interpretability is how far a model’s internal workings can be understood directly; explainability is the broader ability to produce human-understandable accounts of outputs, including for complex models via post-hoc methods. The terms overlap but are not synonymous.
Often heard: A post-hoc explanation reveals the model’s true reasoning.
Actually: Post-hoc explanations are approximations of why a model behaved as it did, not a faithful readout of its internal computation. Treating them as exact can mislead, so explanations should be validated for fidelity.
Often heard: Only highly accurate models need to be explainable.
Actually: Explainability serves oversight, contestability and assurance regardless of accuracy. A high-performing but unexplainable system can still be impossible to challenge, debug or trust for the right reasons.
Going deeper
