On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, Aram Galstyan
Abstract
Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) extrinsic metrics for evaluating fairness in downstream applications and 2) intrinsic metrics for estimating fairness in upstream contextualized language representation models. In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized language models. We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics.
BibTeX
@inproceedings{cao-etal-2022-intrinsic,
title = "On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations",
author = "Cao, Yang Trista and
Pruksachatkun, Yada and
Chang, Kai-Wei and
Gupta, Rahul and
Kumar, Varun and
Dhamala, Jwala and
Galstyan, Aram",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.62/",
doi = "10.18653/v1/2022.acl-short.62",
pages = "561--570"
}