Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an accurate classifier by interactively querying an oracle within a labeling budget. We design algorithms for fair active learning that carefully selects data points to be labeled so as to balance model accuracy and fairness. Specifically, we focus on demographic parity - a widely used measure of fairness. Extensive experiments over benchmark datasets demonstrate the effectiveness of our proposed approach.
@article{arxiv.2006.13025,
title = {Fair Active Learning},
author = {Hadis Anahideh and Abolfazl Asudeh and Saravanan Thirumuruganathan},
journal= {arXiv preprint arXiv:2006.13025},
year = {2020}
}
Comments
This was intended as a replacement of arXiv:2001.01796 please see the updated version there