Fairness for AUC via Feature Augmentation
This addresses fairness issues in classification for disadvantaged groups, but it is incremental as it builds on existing feature augmentation methods.
The paper tackles the problem of fairness in classification measured by AUC, where different protected groups can have significantly varying AUCs, and aims to reduce these cross-group differences by acquiring additional features to improve AUC for the disadvantaged group. The result is a novel approach, fairAUC, which significantly improves AUC for the disadvantaged group relative to benchmarks on synthetic and real-world datasets.
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.