  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, which achieves Pareto efficiency
when trading off performance against fairness. Among various fairness metrics,
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 training problem of a fairness-aware machine learning
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.
