Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Open accessAbstract
Gender bias is highly impacting natural language processing applications.Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources.Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in.In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias.Our analysis includes different measures previously applied in the literature to standard word embeddings.Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.
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Language models inherit biases and dominant views from their training corpus.
“Text suggestions based on language models have been known to inherit biases and dominant 'views' from the text corpus used to train the language model [22,39,43,55].”
From Interacting with Next-Phrase Suggestions: How Suggestion Systems Aid and Influence the Cognitive Processes of Writing (2023) · cites this paper as [22]
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Laricheva, M., Liu, Y., Shi, E., & Wu, A. D. (2024) · British Journal of Psychology · doi:10.1111/bjop.12721 · Cited by 19 · see citing works
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Costa‐jussà, M. R., Lin, P. L., & España-Bonet, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.04778 · Cited by 13 · see citing works
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Smith, E. M., & Williams, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03300 · Cited by 23 · see citing works
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Mulsa, R. A. C., & Spanakis, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00244 · Cited by 14 · see citing works
Bias Detection for Customer Interaction Data: A Survey on Datasets, Methods, and Tools
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Abrar, A., Oeshy, N. T., Kabir, M., & Ananiadou, S. (2025) · AI & Society · doi:10.1007/s00146-025-02721-z · Cited by 15 · see citing works
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Occupational Biases in Norwegian and Multilingual Language Models
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Measuring Gender Bias in Contextualized Embeddings
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GeBioToolkit automatic extraction of gender-balanced multilingual corpus of wikipedia biographies
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Speciesist language and nonhuman animal bias in English Masked Language Models
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Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations
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Inherent Discriminability of BERT Towards Racial Minority Associated Data
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- Authors
- Basta, C., Costa-jussà, M. R., & Casas, N.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- ORCID
- Basta, Christine
- Venue
- Proceedings of the First Workshop on Gender Bias in Natural Language Processing
- Published
- 2019
- Pages
- 33-39
- OpenAlex
- W2972668795
- Cited by
- 189
- References
- 25
- Citations by year
- 2026: 12 · 2025: 25 · 2024: 20 · 2023: 41 · 2022: 32 · 2021: 23 · 2020: 26 · 2019: 10
- FWCI
- 23.69
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Gender Bias in Natural Language Processing (GeBNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR-17-CHR2-0004)
- Ministerio de Economía y Competitividad (TEC2015-69266-P)
- Agència de Gestió d'Ajuts Universitaris i de Recerca
- European Regional Development Fund (TEC2015-69266-P)
- Agencia Estatal de Investigación
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Gender Studies › Gender Studies in Language
- Keywords
- gender bias, contextual word embeddings, word embeddings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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