Fair NLP Models with Differentially Private Text Encoders
Gaurav Maheshwari, Pascal Denis, Mikaela Keller, Aurélien Bellet
Abstract
Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that combines ideas from differential privacy and adversarial training to learn private text representations which also induces fairer models. We empirically evaluate the trade-off between the privacy of the representations and the fairness and accuracy of the downstream model on four NLP datasets. Our results show that FEDERATE consistently improves upon previous methods, and thus suggest that privacy and fairness can positively reinforce each other.
BibTeX
@inproceedings{maheshwari-etal-2022-fair,
title = "Fair {NLP} Models with Differentially Private Text Encoders",
author = "Maheshwari, Gaurav and
Denis, Pascal and
Keller, Mikaela and
Bellet, Aur{\'e}lien",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.514/",
doi = "10.18653/v1/2022.findings-emnlp.514",
pages = "6913--6930"
}