Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns

Zhongbin Xie, Vid Kocijan, Thomas Lukasiewicz, Oana-Maria Camburu


Abstract
Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods. In this work, we focus on evaluating gender bias through coreference resolution, where previous datasets are either hand-crafted or fail to reliably measure an explicitly defined bias. To overcome these shortcomings, we propose a novel method to collect diverse, natural, and minimally distant text pairs via counterfactual generation, and construct Counter-GAP, an annotated dataset consisting of 4008 instances grouped into 1002 quadruples. We further identify a bias cancellation problem in previous group-level metrics on Counter-GAP, and propose to use the difference between inconsistency across genders and within genders to measure bias at a quadruple level. Our results show that four pre-trained language models are significantly more inconsistent across different gender groups than within each group, and that a name-based counterfactual data augmentation method is more effective to mitigate such bias than an anonymization-based method.
Anthology ID:
2023.eacl-main.272
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3761–3773
Language:
URL:
https://aclanthology.org/2023.eacl-main.272
DOI:
10.18653/v1/2023.eacl-main.272
Bibkey:
Cite (ACL):
Zhongbin Xie, Vid Kocijan, Thomas Lukasiewicz, and Oana-Maria Camburu. 2023. Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3761–3773, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns (Xie et al., EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-main.272.pdf
Video:
 https://aclanthology.org/2023.eacl-main.272.mp4