ICML 2025

FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation
Yuheng Lai 1 Leying Guan 2
Abstract
Equalized odds, an important notion of algorith-
mic fairness, aims to ensure that sensitive vari-
ables, such as race and gender, do not unfairly
influence the algorithm’s prediction when condi-
tioning on the true outcome. Despite rapid ad-
vancements, current research primarily focuses
on equalized odds violations caused by a sin-
gle sensitive attribute, leaving the challenge of
simultaneously accounting for multiple attributes
under-addressed. We bridge this gap by intro-
ducing an in-processing fairness-aware learning
approach, FairICP, which integrates adversarial
learning with a novel inverse conditional permu-
tation scheme. FairICP offers a flexible and effi-
cient scheme to promote equalized odds under
fairness conditions described by complex and
multi-dimensional sensitive attributes. The ef-
ficacy and adaptability of our method are demon-
strated through both simulation studies and em-
pirical analyses of real-world datasets.
1. Introduction
Machine learning models are increasingly important in aid-
ing decision-making across various applications. Ensuring
fairness in these models—preventing discrimination against
minorities or other protected groups—remains a significant
challenge (Mehrabi et al., 2021). To address different needs,
several fairness metrics have been developed in the litera-
ture (Mehrabi et al., 2021; Castelnovo et al., 2022). The
equalized odds metric defines fairness by requiring that the
predicted outcome ˆY provides the same level of informa-
tion about the true response Y across different sensitive
attribute(s) A (e.g. gender/race/age) (Hardt et al., 2016):
ˆY ⊥⊥A | Y.
(1)
1Department of Statistics, University of Wisconsin-Madison
2Department of Biostatistics, Yale University. Correspondence to:
Leying Guan <leying.guan@yale.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR...