@inproceedings{harrison-etal-2023-run,
    title = "Run Like a Girl! Sport-Related Gender Bias in Language and Vision",
    author = "Harrison, Sophia  and
      Gualdoni, Eleonora  and
      Boleda, Gemma",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.886",
    doi = "10.18653/v1/2023.findings-acl.886",
    pages = "14093--14103",
    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.",
}

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.
Harrison, Sophia Gualdoni, Eleonora Boleda, Gemma
Run Like a Girl! Sport-Related Gender Bias in Language and Vision