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        <title>Run Like a Girl! Sport-Related Gender Bias in Language and Vision</title>
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        <namePart type="given">Sophia</namePart>
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        <namePart type="family">Gualdoni</namePart>
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Run Like a Girl! Sport-Related Gender Bias in Language and Vision
%A Harrison, Sophia
%A Gualdoni, Eleonora
%A Boleda, Gemma
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F harrison-etal-2023-run
%X 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.
%R 10.18653/v1/2023.findings-acl.886
%U https://aclanthology.org/2023.findings-acl.886
%U https://doi.org/10.18653/v1/2023.findings-acl.886
%P 14093-14103

Markdown (Informal)

[Run Like a Girl! Sport-Related Gender Bias in Language and Vision](https://aclanthology.org/2023.findings-acl.886) (Harrison et al., Findings 2023)

• Run Like a Girl! Sport-Related Gender Bias in Language and Vision (Harrison et al., Findings 2023)

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.