  We study fairness in the context of classification where the performance is
measured by the area under the curve (AUC) of the receiver operating
characteristic. AUC is commonly used to measure the performance of prediction
models. The same classifier can have significantly varying AUCs for different
protected groups and, in real-world applications, it is often desirable to
reduce such cross-group differences. We address the problem of how to acquire
additional features to most greatly improve AUC for the disadvantaged group. We
develop a novel approach, fairAUC, based on feature augmentation (adding
features) to mitigate bias between identifiable groups. The approach requires
only a few summary statistics to offer provable guarantees on AUC improvement,
and allows managers flexibility in determining where in the fairness-accuracy
tradeoff they would like to be. We evaluate fairAUC on synthetic and real-world
datasets and find that it significantly improves AUC for the disadvantaged
group relative to benchmarks maximizing overall AUC and minimizing bias between
groups.
