@inproceedings{li-etal-2022-debiasing,
title = "Debiasing Neural Retrieval via In-batch Balancing Regularization",
author = "Li, Yuantong and
Wei, Xiaokai and
Wang, Zijian and
Wang, Shen and
Bhatia, Parminder and
Ma, Xiaofei and
Arnold, Andrew",
editor = "Hardmeier, Christian and
Basta, Christine and
Costa-juss{\`a}, Marta R. and
Stanovsky, Gabriel and
Gonen, Hila",
booktitle = "Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gebnlp-1.5",
doi = "10.18653/v1/2022.gebnlp-1.5",
pages = "58--66",
abstract = "People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provides a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven{'}t been intuitive objective functions that depend on the click probability and user engagement to directly optimize towards this. In this work, we propose the \textbf{I}n-\textbf{B}atch \textbf{B}alancing \textbf{R}egularization (IBBR) to mitigate the ranking disparity among subgroups. In particular, we develop a differentiable \textbf{normed Pairwise Ranking Fairness} (nPRF) and leverage the T-statistics on top of nPRF over subgroups as a regularization to improve fairness. Empirical results with the BERT-based neural rankers on the MS MARCO Passage Retrieval dataset with the human-annotated non-gendered queries benchmark (CITATION) show that our IBBR method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline.",
}
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<abstract>People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provides a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven’t been intuitive objective functions that depend on the click probability and user engagement to directly optimize towards this. In this work, we propose the In-Batch Balancing Regularization (IBBR) to mitigate the ranking disparity among subgroups. In particular, we develop a differentiable normed Pairwise Ranking Fairness (nPRF) and leverage the T-statistics on top of nPRF over subgroups as a regularization to improve fairness. Empirical results with the BERT-based neural rankers on the MS MARCO Passage Retrieval dataset with the human-annotated non-gendered queries benchmark (CITATION) show that our IBBR method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline.</abstract>
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%0 Conference Proceedings
%T Debiasing Neural Retrieval via In-batch Balancing Regularization
%A Li, Yuantong
%A Wei, Xiaokai
%A Wang, Zijian
%A Wang, Shen
%A Bhatia, Parminder
%A Ma, Xiaofei
%A Arnold, Andrew
%Y Hardmeier, Christian
%Y Basta, Christine
%Y Costa-jussà, Marta R.
%Y Stanovsky, Gabriel
%Y Gonen, Hila
%S Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F li-etal-2022-debiasing
%X People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provides a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven’t been intuitive objective functions that depend on the click probability and user engagement to directly optimize towards this. In this work, we propose the In-Batch Balancing Regularization (IBBR) to mitigate the ranking disparity among subgroups. In particular, we develop a differentiable normed Pairwise Ranking Fairness (nPRF) and leverage the T-statistics on top of nPRF over subgroups as a regularization to improve fairness. Empirical results with the BERT-based neural rankers on the MS MARCO Passage Retrieval dataset with the human-annotated non-gendered queries benchmark (CITATION) show that our IBBR method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline.
%R 10.18653/v1/2022.gebnlp-1.5
%U https://aclanthology.org/2022.gebnlp-1.5
%U https://doi.org/10.18653/v1/2022.gebnlp-1.5
%P 58-66
Markdown (Informal)
[Debiasing Neural Retrieval via In-batch Balancing Regularization](https://aclanthology.org/2022.gebnlp-1.5) (Li et al., GeBNLP 2022)
ACL
- Yuantong Li, Xiaokai Wei, Zijian Wang, Shen Wang, Parminder Bhatia, Xiaofei Ma, and Andrew Arnold. 2022. Debiasing Neural Retrieval via In-batch Balancing Regularization. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 58–66, Seattle, Washington. Association for Computational Linguistics.