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AI & Machine Learning · Foundational

Explainability

The ability to describe why an AI model produced a particular output in terms humans can understand.

Definition

Explainability supports trust, debugging, fairness review and regulatory rights to explanation. Techniques include feature importance, SHAP, LIME, counterfactuals and attention or citation inspection. Inherently interpretable models such as linear models and small trees may be preferred in regulated settings.

Exam tip

Regulated decision (credit, hiring) plus need to justify outcomes: choose explainable models or SHAP-based explanations.

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