Explainability Techniques
Methods used to interpret and understand the decisions made by AI models, such as LIME, SHAP, and saliency maps.
Definition
A toolbox of model-agnostic (LIME, SHAP) and model-specific (saliency for CNNs, attention visualization) approaches that generate feature attributions, visualize decision pathways, and produce counterfactual explanations. Governance best practices include selecting techniques appropriate to the model type, validating explanation accuracy, and integrating explanations into end-user workflows.
Real-World Example
A retail chain uses SHAP to explain its customer-churn predictions: each record shows “high monthly spend” and “recent service complaints” as top contributors. Customer-success managers use these explanations to tailor retention offers, improving customer satisfaction.
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