The capability of an AI system to make its decisions or outputs understandable to humans in terms they can comprehend. Explainability techniques help users understand why an AI system produced a particular output or recommendation.

AI explainability addresses the “black box” problem of complex AI systems, particularly deep learning models whose internal operations can be difficult to interpret. Explainability is crucial for building trust, enabling effective human oversight, facilitating regulatory compliance, and supporting recourse for individuals affected by AI decisions. Approaches range from using inherently interpretable models to applying post-hoc explanation techniques that approximate how complex models reach specific conclusions.

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A loan decision system that provides loan officers with clear explanations of the key factors influencing its recommendation, highlighting which applicant characteristics most significantly affected the risk assessment and how they compare to the overall applicant population.