Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
Human-like social bias of pre-trained language models (PLMs) on downstream tasks have attracted increasing attention. The potential flaws in the training data are the main factor that causes unfairness in PLMs. Existing data-centric debiasing strategies mainly leverage explicit bias words (defined as sensitive attribute words specific to demographic groups) for counterfactual data augmentation to balance the training data. However, they lack consideration of implicit bias words potentially associated with explicit bias words in complex distribution data, which indirectly harms the fairness of PLMs. To this end, we propose a Data-Centric Debiasing method (named Data-Debias), which uses an explainability method to search for implicit bias words to assist in debiasing PLMs. Specifically, we compute the feature attributions of all tokens using the Integrated Gradients method, and then treat the tokens that have a large impact on the model's decision as implicit bias words. To make the search results more precise, we iteratively train a biased model to amplify the bias with each iteration. Finally, we use the implicit bias words searched in the last iteration to assist in debiasing PLMs. Extensive experimental results on multiple PLMs debiasing on three different classification tasks demonstrate that Data-Debias achieves state-of-the-art debiasing performance and strong generalization while maintaining predictive abilities.
Identifier
85205292363 (Scopus)
ISBN
[9798891760998]
Publication Title
Proceedings of the Annual Meeting of the Association for Computational Linguistics
External Full Text Location
https://doi.org/10.18653/v1/2024.findings-acl.226
ISSN
0736587X
First Page
3773
Last Page
3786
Grant
20240402067GH
Fund Ref
International Science and Technology Cooperation Program of Jiangsu Province
Recommended Citation
Li, Yingji; Du, Mengnan; Song, Rui; Wang, Xin; and Wang, Ying, "Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models" (2024). Faculty Publications. 859.
https://digitalcommons.njit.edu/fac_pubs/859
