Fairness for AUC via Feature Augmentation

Autor:	Fong, Hortense, Kumar, Vineet, Mehrotra, Anay, Vishnoi, Nisheeth K.
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computers and Society Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: This is the full version of a non-archival paper accepted for presentation at ACM FAccT 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.12823 Zobrazit plný text záznamu View this record from Arxiv