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Gender bias in Language and Vision datasets and models has the potential toperpetuate harmful stereotypes and discrimination. We analyze gender bias intwo Language and Vision datasets. Consistent with prior work, we find that bothdatasets underrepresent women, which promotes their invisibilization. Moreover,we hypothesize and find that a bias affects human naming choices for peopleplaying sports: speakers produce names indicating the sport (e.g. 'tennisplayer' or 'surfer') more often when it is a man or a boy participating in thesport than when it is a woman or a girl, with an average of 46% vs. 35% ofsports-related names for each gender. A computational model trained on thesenaming data reproduces the bias. We argue that both the data and the modelresult in representational harm against women.

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