@inproceedings{leteno-etal-2023-fair,
title = "Fair Text Classification with {W}asserstein Independence",
author = "Leteno, Thibaud and
Gourru, Antoine and
Laclau, Charlotte and
Emonet, R{\'e}mi and
Gravier, Christophe",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.978",
doi = "10.18653/v1/2023.emnlp-main.978",
pages = "15790--15803",
abstract = "Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g. women vs. men) remains an open challenge. This paper presents a novel method for mitigating biases in neural text classification, agnostic to the model architecture. Considering the difficulty to distinguish fair from unfair information in a text encoder, we take inspiration from adversarial training to induce Wasserstein independence between representations learned to predict our target label and the ones learned to predict some sensitive attribute. Our approach provides two significant advantages. Firstly, it does not require annotations of sensitive attributes in both testing and training data. This is more suitable for real-life scenarios compared to existing methods that require annotations of sensitive attributes at train time. Secondly, our approach exhibits a comparable or better fairness-accuracy trade-off compared to existing methods.",
}
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<abstract>Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g. women vs. men) remains an open challenge. This paper presents a novel method for mitigating biases in neural text classification, agnostic to the model architecture. Considering the difficulty to distinguish fair from unfair information in a text encoder, we take inspiration from adversarial training to induce Wasserstein independence between representations learned to predict our target label and the ones learned to predict some sensitive attribute. Our approach provides two significant advantages. Firstly, it does not require annotations of sensitive attributes in both testing and training data. This is more suitable for real-life scenarios compared to existing methods that require annotations of sensitive attributes at train time. Secondly, our approach exhibits a comparable or better fairness-accuracy trade-off compared to existing methods.</abstract>
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%0 Conference Proceedings
%T Fair Text Classification with Wasserstein Independence
%A Leteno, Thibaud
%A Gourru, Antoine
%A Laclau, Charlotte
%A Emonet, Rémi
%A Gravier, Christophe
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F leteno-etal-2023-fair
%X Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g. women vs. men) remains an open challenge. This paper presents a novel method for mitigating biases in neural text classification, agnostic to the model architecture. Considering the difficulty to distinguish fair from unfair information in a text encoder, we take inspiration from adversarial training to induce Wasserstein independence between representations learned to predict our target label and the ones learned to predict some sensitive attribute. Our approach provides two significant advantages. Firstly, it does not require annotations of sensitive attributes in both testing and training data. This is more suitable for real-life scenarios compared to existing methods that require annotations of sensitive attributes at train time. Secondly, our approach exhibits a comparable or better fairness-accuracy trade-off compared to existing methods.
%R 10.18653/v1/2023.emnlp-main.978
%U https://aclanthology.org/2023.emnlp-main.978
%U https://doi.org/10.18653/v1/2023.emnlp-main.978
%P 15790-15803
Markdown (Informal)
[Fair Text Classification with Wasserstein Independence](https://aclanthology.org/2023.emnlp-main.978) (Leteno et al., EMNLP 2023)
ACL
- Thibaud Leteno, Antoine Gourru, Charlotte Laclau, Rémi Emonet, and Christophe Gravier. 2023. Fair Text Classification with Wasserstein Independence. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15790–15803, Singapore. Association for Computational Linguistics.