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Interpretability / Explainability (XAI)
Fundamentals
Understanding how and why a model makes decisions.
Explainable AI (XAI) makes model behavior understandable to humans.
- Approaches: Intrinsically interpretable models (e.g., trees) and post-hoc methods (SHAP, LIME, Grad-CAM).
- Goals: Trust, debugging, compliance, safety.
- Limitations: Approximations can mislead; explanations must fit the target audience.