AI ethics bias detection fairness algorithms

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.