Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints

Yao Yao, Qihang Lin, Tianbao Yang

Abstract:

As machine learning being used increasingly in
making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to
discriminatory decisions for protected population.
A direct approach for obtaining a fair predictive
model is to train the model through optimizing
its prediction performance subject to fairness constraints. Among various fairness constraints, the
ones based on the area under the ROC curve
(AUC) are emerging recently because they are
threshold-agnostic and effective for unbalanced
data. In this work, we formulate the problem
of training a fairness-aware predictive model as
an AUC optimization problem subject to a class
of AUC-based fairness constraints. This problem can be reformulated as a min-max optimization problem with min-max constraints, which
we solve by stochastic first-order methods based
on a new Bregman divergence designed for the
special structure of the problem. We numerically
demonstrate the effectiveness of our approach on
real-world data under different fairness metrics.