Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection

Indira Sen, Mattia Samory, Claudia Wagner, Isabelle Augenstein


Abstract
Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an indicator of model robustness. The improvement is credited to promoting core features of the construct over spurious artifacts that happen to correlate with it. Yet, over-relying on core features may lead to unintended model bias. Especially, construct-driven CAD—perturbations of core features—may induce models to ignore the context in which core features are used. Here, we test models for sexism and hate speech detection on challenging data: non-hate and non-sexist usage of identity and gendered terms. On these hard cases, models trained on CAD, especially construct-driven CAD, show higher false positive rates than models trained on the original, unperturbed data. Using a diverse set of CAD—construct-driven and construct-agnostic—reduces such unintended bias.
Anthology ID:
2022.naacl-main.347
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4716–4726
Language:
URL:
https://aclanthology.org/2022.naacl-main.347
DOI:
10.18653/v1/2022.naacl-main.347
Bibkey:
Cite (ACL):
Indira Sen, Mattia Samory, Claudia Wagner, and Isabelle Augenstein. 2022. Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4716–4726, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection (Sen et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.347.pdf
Software:
 2022.naacl-main.347.software.zip