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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...
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...
