Run Like a Girl! Sport-Related Gender Bias in Language and Vision (2023)

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.

## introduction existing social biases and stereotypes against certain groups, such as women and racial minorities, are known to be reproduced by computational models @xcite @xcite @xcite . this is primarily due to the fact that the datasets that the models are trained on are biased themselves, because, unless explicit steps are taken, datasets tend to mirror social biases @xcite @xcite . moreover, models often amplify biases, because they overrely on shallow patterns and lean towards majority labels @xcite @xcite . bias in ai has ethical implications because it can result in harm for the affected groups: both representational harm, with systems demeaning or ignoring them, and allocational harm, with systems allocating fewer resources or opportunities to them @xcite @xcite . for instance, the fact that the multimodal model vl-bert @xcite often predicts that a woman carrying a briefcase is carrying a purse @xcite constitutes representational harm, with working women not being recognized as such. figure 1 : images of people playing sports from the manynames dataset, together with the names that human annotators produced and their counts. in this paper, we focus on gender bias that causes representational harm for women, specifically for women in sports, in the area of language and vision (l&v). previous work on gender bias in l&v has shown that bias often relates to the language component, causing models to override the specific visual information in classification decisions @xcite @xcite and vice versa @xcite , such as in the 'purse/briefcase' example above. we examine bias that is present in the language, without examining its interaction with the visual component in the model. women in western societies have traditionally been marginalized, excluded, and deterred from participating in sports or even physical activity, while men have been encouraged @xcite @xcite . sport has long been stereotypically associated with masculinity, and females have been thought physically incapable of performing well in this area @xcite . participation in sports by women and girls has continued to increase since the 1960s in parallel with other social advances, however, rarely do women rise to managerial or coaching roles, even in women's teams @xcite . women's sports are also underrepresented in the media, which has been linked to perpetuating the stereotype of the male athlete over the female @xcite . we analyze the data in a language and vision dataset for object naming, manynames @xcite ; more information in the next section). figure 1 shows two example images in many-names together with the names elicited from subjects. how we name an object or entity is intimately linked to how we conceptualize it @xcite . we hypothesize that due to the social constraints discussed above, speakers produce a sports-related name such as 'surfer' less often when their referent is a woman, that is, they do not conceptualize female athletes as athletes. if the bias indeed exists in the speaker population, we expect it to be present in the naming data and propagate to computational models; we check both expectations. before that, we examine whether there is an overall representational bias in manynames and its parent dataset, visualgenome @xcite , with women being underrepresented. visualgenome contains 108k images that are the intersection of images in the datasets yfcc100m @xcite and ms-coco @xcite . the objects in each visu-algenome image were manually identified, labeled, and linked to their corresponding wordnet synset (visualgenome provides many more annotations, but these are the ones of interest for the present study). both visualgenome and manynames employed crowd-sourced workers from amazon mechanical turk (amt) as annotators foot_0 for the images, with workers coming predominantly from the usa. note that while the images in manynames are a subset of those in visualgenome, the naming annotations were collected afresh for manynames. the yfcc100m images were all those uploaded to flickr between 2004 and 2014, published under a commercial or noncommercial license. ms-coco contains all images from flickr available at the time of the dataset construction that belonged to 91 predefined image categories (e.g., horse, people, and laptop). images on flickr are uploaded by flickr users. to check for representational bias, we extracted all objects in visualgenome (image areas within a bounding box) with labels corresponding to the four most common gender-associated names: 'boy', 'girl', 'man', and 'woman'. for manynames, we used the same names and the full naming distribution. ## discussion and conclusions we have identified pervasive biases against women in language & vision. our main contribution is the individuation of a bias that characterizes human naming choices and therefore the naming data available for models. while we have focused on manynames, this issue is likely to affect other datasets containing names, such as widely used datasets for captioning @xcite @xcite and referring expression generation @xcite . the bias concerns the kind of name chosen for athletes, depending on the genre: people produce fewer sports-related names for females playing sports (average 35%) than for males playing sports (46%). as far as we know, this kind of bias has not been previously discussed, and it is, we argue, more implicit and thus difficult to identify than other kinds of bias that are more commonly discussed in the literature, such as unbalanced classes in datasets due to the underrepresentation of certain groups (which we also found and discuss below). we find the naming bias both in the human data of manynames and a model trained on these data. thus, even women who do play sports (which are fewer to begin with, as mentioned in section 3) are not conceptualized as such. this constitutes representational harm and contributes to limiting choices for women. we also find that women are underrepresented in l&v, pronounced especially in visualgenome, which has over twice as many images of males than females. moreover, the proportion of males and females playing sports in manynames is skewed, with only 30.2% of the pictures of people playing sports depicting females. as mentioned above, the actual percentage of women and girls in sports in anglo-saxon societies is closer to 45%, according to recent data @xcite . the underrepresentation of social groups is harmful itself @xcite , as well as because models trained on unbalanced data can neglect crucial patterns relevant to those within the group @xcite . given how the images were selected (see section 2), the origin of the underrepresentation of women in these datasets must come from the kinds of images uploaded onto flickr around the 2010s. this is in turn is likely rooted in the demographic characteristics of flickr users during that period and to the fact that, in general, the internet has historically been heavily male-dominated @xcite . this is a serious concern, as most resources used in l&v (as well as computational linguistics and ai in general) come from the internet. our findings are in line with other research on gender bias, both in l&v and in nlp and ai more broadly, as discussed in the introduction. they also resonate with a study by the pew research center @xcite showing that results in google image search underrepresent women in various jobs compared with their actual participation in those jobs in the usa according to the bureau of labor statistics. ultimately, based on the findings of this paper, it can be concluded that as far as the datasets and model analyzed are concerned, when it comes to sports: a man with a tennis racket is a tennis player. a woman with a tennis racket is just a woman with a tennis racket.