FairTree audits ML models for subgroup fairness by decomposing performance disparities into systematic bias and variance using permutation-based and fluctuation tests adapted from psychometric methods.
Model-Based Recursive Partitioning
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Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.
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FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition
FairTree audits ML models for subgroup fairness by decomposing performance disparities into systematic bias and variance using permutation-based and fluctuation tests adapted from psychometric methods.
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The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB, and GUIDE
Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.