User Guide# Fairness in Machine Learning Fairness of AI systems Types of harms Concept glossary Construct validity Fairness assessment and unfairness mitigation Group fairness, sensitive features Parity constraints Disparity metrics, group metrics What traps can we fall into when modeling a social problem? The Solutionism Trap The Ripple Effect Trap The Formalism Trap The Portability Trap The Framing Trap References Assessment Performing a Fairness Assessment Identify types of harms Identify the groups that might be harmed Quantify harms Disaggregated metrics Compare quantified harms across the groups Predefined fairness metrics Common fairness metrics Demographic parity Equalized odds Equal opportunity The Four Fifths Rule: Often Misapplied Summary References Defining custom fairness metrics Intersecting Groups Multiple Sensitive Features Control Features Confidence Interval Estimation Bootstrapping Bootstrapping MetricFrame Summary Advanced Usage of MetricFrame Extra Arguments to Metric functions More Complex Metrics Plotting Plotting grouped metrics Saving and loading MetricFrame Fairlearn dashboard Mitigations Preprocessing Correlation Remover Postprocessing Threshold optimizer References Reductions Fairness constraints for binary classification Fairness constraints for multiclass classification Fairness constraints for regression References Adversarial Mitigation Models Data types and loss functions Training References Datasets Adult Census Dataset ACSIncome Revisiting the Boston Housing Dataset Introduction Dataset Origin and Use Dataset Issues Fairness-related harms assessment Discussion References Diabetes 130-Hospitals Dataset Introduction Dataset Description Using the dataset Installation and version guide Installation Guide Installation Dependencies Version guide v0.1 v0.10.0 v0.11.0 v0.12.0 v0.2.0 v0.3.0 v0.4.0 v0.4.1 v0.4.2 v0.4.3 v0.4.4 v0.4.5 v0.4.6 v0.5.0 v0.6.0 v0.6.1 v0.6.2 v0.7.0 v0.8.0 v0.9.0 Further Resources Books Papers Online demos, talks, tutorials
