Run Like a Girl! Sport-Related Gender Bias in Language and Vision

Sophia Harrison, Eleonora Gualdoni, Gemma Boleda

Abstract
Gender bias in Language and Vision datasets and models has the potential to perpetuate harmful stereotypes and discrimination. We analyze gender bias in two Language and Vision datasets. Consistent with prior work, we find that both datasets underrepresent women, which promotes their invisibilization. Moreover, we hypothesize and find that a bias affects human naming choices for people playing sports: speakers produce names indicating the sport (e.g. “tennis player” or “surfer”) more often when it is a man or a boy participating in the sport than when it is a woman or a girl, with an average of 46% vs. 35% of sports-related names for each gender. A computational model trained on these naming data reproduces thebias. We argue that both the data and the model result in representational harm against women.
Anthology ID:
2023.findings-acl.886
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14093–14103
Language:
URL:
https://aclanthology.org/2023.findings-acl.886
DOI:
10.18653/v1/2023.findings-acl.886
Bibkey:
Cite (ACL):
Sophia Harrison, Eleonora Gualdoni, and Gemma Boleda. 2023. Run Like a Girl! Sport-Related Gender Bias in Language and Vision. In Findings of the Association for Computational Linguistics: ACL 2023, pages 14093–14103, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Run Like a Girl! Sport-Related Gender Bias in Language and Vision (Harrison et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-acl.886.pdf
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