Editing Explainable AI (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Explainability''' — The degree to which a model's behavior can be understood and explained to humans.
* '''Interpretability''' — The degree to which the internal mechanisms of a model can be directly understood; often used interchangeably with explainability.
* '''Black-box model''' — A model whose internal workings are opaque; predictions are produced but the reasoning is not accessible (deep neural networks, ensemble methods).
* '''Glass-box model''' — An inherently interpretable model whose reasoning is transparent by design (linear regression, decision trees, rule lists).
* '''Post-hoc explanation''' — An explanation generated after a model is trained, attempting to approximate or explain its behavior (SHAP, LIME).
* '''SHAP (SHapley Additive exPlanations)''' — A game-theoretic framework that assigns each feature a contribution value for each prediction.
* '''LIME (Local Interpretable Model-agnostic Explanations)''' — A technique that approximates complex model behavior locally with a simple interpretable model.
* '''Feature importance''' — A ranking of input features by their overall contribution to the model's predictions.
* '''Saliency map''' — A visualization highlighting which input pixels or regions most influenced a neural network's output on an image.
* '''Grad-CAM''' — A gradient-based visualization technique showing which image regions a CNN attended to for a prediction.
* '''Counterfactual explanation''' — An explanation of the form "if X had been different, the prediction would have changed to Y."
* '''Anchors''' — Rule-based explanations that identify sufficient conditions guaranteeing a prediction regardless of changes to other features.
* '''Model card''' — A documentation framework that describes a model's intended use, performance, and limitations to stakeholders.
* '''Right to explanation''' — A legal concept (GDPR Article 22) granting individuals the right to understand automated decisions affecting them.
* '''Faithfulness''' — The degree to which an explanation accurately reflects the model's actual reasoning process.
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