Learning Antidote Data to Individual Unfairness
- URL: http://arxiv.org/abs/2211.15897v3
- Date: Wed, 24 May 2023 04:56:59 GMT
- Title: Learning Antidote Data to Individual Unfairness
- Authors: Peizhao Li, Ethan Xia, Hongfu Liu
- Abstract summary: Individual fairness is a vital notion to describe fair treatment for individual cases.
Previous studies characterize individual fairness as a prediction-invariant problem.
We show our method resists individual unfairness at a minimal or zero cost to predictive utility.
- Score: 23.119278763970037
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Fairness is essential for machine learning systems deployed in high-stake
applications. Among all fairness notions, individual fairness, deriving from a
consensus that `similar individuals should be treated similarly,' is a vital
notion to describe fair treatment for individual cases. Previous studies
typically characterize individual fairness as a prediction-invariant problem
when perturbing sensitive attributes on samples, and solve it by
Distributionally Robust Optimization (DRO) paradigm. However, such adversarial
perturbations along a direction covering sensitive information used in DRO do
not consider the inherent feature correlations or innate data constraints,
therefore could mislead the model to optimize at off-manifold and unrealistic
samples. In light of this drawback, in this paper, we propose to learn and
generate antidote data that approximately follows the data distribution to
remedy individual unfairness. These generated on-manifold antidote data can be
used through a generic optimization procedure along with original training
data, resulting in a pure pre-processing approach to individual unfairness, or
can also fit well with the in-processing DRO paradigm. Through extensive
experiments on multiple tabular datasets, we demonstrate our method resists
individual unfairness at a minimal or zero cost to predictive utility compared
to baselines.
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