Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Exploration, detection, and mitigation: Unveiling gender bias in NLP

Chunxiao Zhang * 1
1 University of California

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 62-68
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Chunxiao Zhang. Exploration, detection, and mitigation: Unveiling gender bias in NLP. ACE (2024) Vol. 52: 62-68. DOI: 10.54254/2755-2721/52/20241234.

Abstract

Natural Language Processing (NLP) systems have a mundane impact, yet they harbour either obvious or potential gender bias. The automation of decision-making in NLP models even exacerbates unfair treatment. In recent years, researchers have started to notice this issue and have made some approaches to detect and mitigate these biases, yet no consensus on the approaches exists. This paper discusses the interdisciplinary field of linguistics and computer sciences by presenting the most common gender bias categories and breaking them down with ethical and artificial intelligence approaches. Specific methods for detecting and minimizing bias are shown around biases present in raw data, annotator, model, and the linguistic gender system. In this paper, an overview of the hotspots and future perspectives of this research topic is presented. Limitations of some detection methods are pinpointed, providing novel insights into future research.

Keywords

Gender Bias, Bias Detection, Bias Mitigation, Ethics in NLP

References

1. Font J and Costa-jussà M 2019 Equalizing gender biases in neural machine translation with word embeddings techniques Preprint arXiv:1901.03116

2. Nemani P, Joel Y, Vijay P and Liza F 2023 Gender bias in transformer models: a comprehensive survey Preprint arXiv:2306:10530 (Yericherla Deepak Koel, Farhama Ferdousi Liza)

3. Nadeem A, Sbedin B and Marjanovic O 2020 Gender bias in AI: a review of contributing factors and mitigating strategies ACIS 2020 Proceedings 27

4. Dastin J 2022 Amazon Scraps Secret AI Recuiting Tool that Showed Bias against Women vol 1

5. Zhao J, Wang T, Yakstar M, Cotterell R, Ordonez and Chang K 2019 Gender bias in contextualized word embeddings Preprint arXiv:1904.03310

6. Piazzolla S, Savoldi B and Ventivogli L 2023 Good, but not always fair: an evaluation of gender bias for three commercial machine translation systems Preprint arXiv:2306.05882

7. Cabrera L and Niehues J 2023 Gender lost in translation: how bridging the gap between languages affects gender bias in zero-shot multilingual translation Preprint arXiv:2305.16935

8. Bolukbasi T, Chang K, Zou J, Saligrama V and Kalai A 2016 Man is to computer programmer as woman is to homemaker? debiasing word embeddings Preprint arXiv:1607.06520

9. Ali M, Sapiezynski P, Bogen M, Korolova A, Mislove A and Rieke A 2019 Discrimination through optimization: how Facebook’s ad delivery can lead to skewed outcomes Preprint arXiv:1904.02095

10. Kiritchenko S and Mohammad S 2018 Examining gender and race bias in two hundred sentiment analysis systems Preprint arXiv:1805.04508

11. O’Neil C 2016 Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy vol 1

12. Cho W, Kim J, Yang J and Kim N 2021 Towards cross-lingual generalization of translation gender bias ACM. pp 449-57 (Won Ik Cho, Nam Soo Kim)

13. Caliskan A, Bryson J, Narayanan A 2017 Semantics derived automatically from language corpora contain human-like biases Science vol. 356 no. 6334 pp 183-6 (Joanna J. Bryson)

14. Xie Z, Kocijan V, Lukasiewicz and Camburu O 2023 Counter-GAP: counterfactual bias evaluation through gendered ambiguous pronouns Preprint arXiv:2302.05674

15. Wang J, Rubinstein B and Cohn T 2022 Measuring and mitigating name biases in neural machine translation ACL. Vol. 1 2576-90 (Benjamin I. P. Rubinstein)

16. Abbasi A, Li J, Clifford G and Taylor H 2018 Make “fairness by design” part of machine learning Harvard Business Review

17. Li Y, Wei X, Wang Z, Wang S, Bhatia P, Ma X and Arnold A 2022 Debiasing neural retrieval via in-batch balancing regularization Preprint arXiv:2205.09240

18. Piergentili A, Fucci D, Savoldi B, Bentivogli L and Negri M 2023 Gender neutralization for an inclusive machine translation: from theoretical foundations to open challenges Preprint arXiv:2301.10075

19. Saunders D and Olsen K 2023 Gender, names and other mysteries: towards the ambiguous for gender-inclusive translation Preprint arXiv:2306.04573

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241234
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated