fairlearn-bias-detector

# fairlearn-bias-detector ## Overview Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting in ML models. ## Capabilities - Demographic parity assessment - Equalized odds evaluation - Disparity metrics calculation - Bias mitigation algorithms (preprocessing, in-processing, post-processing) - Fairness constraint optimization - Compliance documentation generation - Intersectional fairness analysis - Threshold optimization for fairness ## Target Processes - Model Evaluation and Validation Framework - Model Interpretability and Explainability Analysis - A/B Testing Framework for ML Models ## Tools and Libraries - Fairlearn - scikit-learn - pandas ## Input Schema ```json { "type": "object", "required": ["modelPath", "dataPath", "sensitiveFeatures"], "properties": { "modelPath": { "type": "string", "description": "Path to the trained model" }, "dataPath": { "type": "string", "description": "Path to evaluation data" }, "sensitiveFeatures": { "type": "array", "items": { "type": "string" }, "description": "Column names of sensitive attributes" }, "labelColumn": { "type": "string", "description": "Name of the target/label column" }, "assessmentConfig": { "type": "object", "properties": { "metrics": { "type": "array", "items": { "type": "string", "enum": ["demographic_parity"...