AI Glossary

Explainable AI

Explainable AI (XAI) methods help people understand why a model produced a given output, within technical limits of fidelity.

Definition

Explainable AI (XAI) methods help people understand why a model produced a given output, within technical limits of fidelity.

Plain English explanation

Tools and techniques that try to make a model’s decisions more understandable to humans.

Technical explanation

Methods include intrinsically interpretable models and post-hoc explanations (feature attributions, prototypes, counterfactuals). Explanations can be faithful to a local approximation without fully describing the model.

Why it matters

Regulated industries often require understandable decision factors for high-impact outcomes.

Real-world applications

  • Credit and insurance decision support
  • Clinical decision support review
  • Model debugging

Benefits

  • Supports human oversight
  • Aids debugging and trust calibration

Limitations

  • Explanations can mislead if oversold
  • Tradeoffs with accuracy for some interpretable models

Common misconceptions

  • A heatmap is not proof of causal understanding
Interpretability Responsible AI

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FAQ

Is XAI required by law?

Requirements vary by jurisdiction and use case. Brel cites frameworks; it does not provide legal advice.

Last reviewed

Sources

  • Guidotti et al. — survey of explanation methods

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