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Fairness in Machine Learning
Assessment
Mitigation
Datasets
Adult Census Dataset
ACSIncome
Revisiting the Boston Housing Dataset
Installation and version guide
Installation Guide
Version guide
v0.1
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
Further Resources
User Guide
ΒΆ
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
Introduction
Identify types of harms
Identify the groups that might be harmed
Quantify harms
Compare quantified harms across the groups
Disaggregated metrics
Disaggregated metrics using
MetricFrame
Multiple metrics in a single
MetricFrame
Non-sample parameters
Multiclass metrics
Multiple sensitive features
Scalar results from
MetricFrame
Control features for grouped metrics
Plotting
Plotting grouped metrics
Fairlearn dashboard
Mitigation
Preprocessing
Correlation Remover
Postprocessing
Reductions
Fairness constraints for binary classification
Fairness constraints for multiclass classification
Fairness constraints for regression
Exponentiated Gradient
Grid Search
References
Datasets
Adult Census Dataset
ACSIncome
Revisiting the Boston Housing Dataset
Introduction
Dataset Origin and Use
Dataset Issues
Fairness-related harms assessment
Discussion
Installation and version guide
Installation Guide
Installation
Dependencies
Version guide
v0.1
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
Further Resources
Books
Papers
Online demos, talks, tutorials