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.
Shenkman, Jiang Bian, and Fei Wang
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
citing papers explorer
-
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.
-
Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.