#explainable-ai

Implement model interpretability and explainable AI techniques for understanding machine learning model decisions and building trust. Interpretability types: 1. Global interpretability: overall model behavior, feature importance, decision boundary visualization. 2. Local interpretability: individual prediction explanations, instance-specific feature contributions. 3. Post-hoc interpretability: model-agnostic explanations, surrogate models, perturbation-based methods. LIME (Local Interpretable Model-agnostic Explanations): 1. Perturbation strategy: modify input features, observe prediction changes, local linear approximation. 2. Instance selection: neighborhood definition, sampling strategy, interpretable representation. 3. Explanation generation: simple model fitting, feature importance scores, visualization. SHAP (SHapley Additive exPlanations): 1. Game theory foundation: Shapley values, fair attribution, additive feature importance. 2. SHAP variants: TreeSHAP for tree models, KernelSHAP (model-agnostic), DeepSHAP for neural networks. 3. Visualization: waterfall plots, beeswarm plots, force plots, summary plots. Attention mechanisms: 1. Self-attention: transformer attention weights, token importance visualization. 2. Visual attention: CNN attention maps, grad-CAM, saliency maps for image models. 3. Attention interpretation: head analysis, layer-wise attention, attention rollout. Feature importance methods: 1. Permutation importance: feature shuffling, prediction degradation measurement, model-agnostic. 2. Integrated gradients: path integration, gradient-based attribution, baseline selection. 3. Ablation studies: feature removal, systematic evaluation, causal analysis. Model-specific interpretability: decision trees (rule extraction), linear models (coefficient analysis), ensemble methods (feature voting), deep learning (layer analysis), evaluation metrics for explanation quality and user trust assessment.

Implement ethical AI practices with bias detection, fairness assessment, and responsible machine learning development. Bias detection methods: 1. Statistical parity: equal positive prediction rate across groups, demographic parity constraint. 2. Equalized odds: equal true positive and false positive rates across groups. 3. Individual fairness: similar individuals receive similar predictions, Lipschitz constraint. 4. Counterfactual fairness: predictions unchanged in counterfactual world without sensitive attributes. Data bias assessment: 1. Representation bias: underrepresented groups in training data, sampling strategies. 2. Historical bias: past discriminatory practices encoded in data, temporal analysis. 3. Measurement bias: different data quality across groups, feature reliability assessment. Fairness metrics: 1. Demographic parity: P(Y_hat=1|A=0) = P(Y_hat=1|A=1), group-level fairness. 2. Equal opportunity: TPR consistency across groups, focus on positive outcomes. 3. Calibration: prediction confidence matches actual outcomes across groups. Mitigation strategies: 1. Pre-processing: data augmentation, re-sampling, synthetic data generation (SMOTE). 2. In-processing: fairness constraints during training, adversarial debiasing. 3. Post-processing: threshold adjustment, prediction calibration, outcome redistribution. Explainable AI (XAI): 1. LIME: local interpretable model-agnostic explanations, feature importance visualization. 2. SHAP: unified framework, game theory approach, additive feature attributions. 3. Attention mechanisms: model-internal explanations, highlight important input regions. Governance framework: ethics review board, algorithmic impact assessments, regular auditing (quarterly), documentation requirements, stakeholder involvement in design process.