explainable-ai

## What I do - Apply XAI techniques to models - Generate model-agnostic explanations - Create interpretable features - Build trust in AI systems - Handle fairness and bias ## When to use me Use me when: - Regulatory compliance (GDPR, EU AI Act) - High-stakes decisions - Debugging model behavior - Building trust with users ## Key Concepts ### XAI Methods - **SHAP**: Shapley additive explanations - **LIME**: Local interpretable model-agnostic - **Counterfactuals**: What-if analysis - **Feature importance**: Permutation, tree-based ### SHAP Framework ```python import shap # Kernel SHAP for any model explainer = shap.KernelExplainer(model.predict, X_background) shap_values = explainer.shap_values(X_test) # Tree SHAP for tree-based explainer = shap.TreeExplainer(model) shap_values = explainer.shap_values(X_test) # Visualization shap.force_plot( explainer.expected_value, shap_values[0,:], X_test.iloc[0,:] ) ``` ### Counterfactual Explanations ```python from dice_ml import Explainer # Create explainer explainer = Explainer(model, X_train, outcome_class="target") # Generate counterfactuals explanation = explanation.generate_counterfactuals( X_test[0:1], total_CFs=3, desired_class="opposite" ) ``` ### Fairness - Demographic parity - Equalized odds - Individual fairness - Bias detection metrics