Advancements in natural language processing have spurred the proliferation of studies examining gender stereotypes in online texts, including news and social media. Yet, while these studies suggest a reduction of gender bias in recent years, research indicates that progress toward gender equality has slowed or stalled in vital areas of social life, from hiring practices to household management. Textual measures of online stereotypes are at risk of underestimating the gender gap, which may be more salient in online images that visualize the demographics of people. In this talk, I show that online gender stereotypes are more prevalent in images than texts using a novel dataset comprising over one million images from Google, Wikipedia, and IMDb, mapped to over 3,400 distinct social categories, including occupations (e.g., “doctor”) as well as generic social roles (e.g., “friend”) and lifestyles (e.g., “vegan”); stereotypes in these images are then compared to stereotypes measured by word embedding models trained on billions of words from online texts. To characterize the empirical consequences of this finding, I use an online experiment to show that googling for visual rather than textual descriptions of occupations amplifies people’s implicit bias toward associating men with science and women with liberal arts, a stereotype linked to pervasive inequalities in academia and industry. I conclude by showing how text and images can differ in the kind of stereotypes they encode; for example, I show that gendered ageism, whereby women are pressured to appear younger than men, is particularly pervasive in online images. Implications for algorithmic bias are discussed.

The Chinese Cultural Revolution (1966–1976) killed nearly two million people. It produced a sprawling and unstable landscape of violence wherein victim and victimizer were often interchangeable roles. How did everyday resistance against the campaign look like? Focusing on the most vulnerable targets of discipline and punishment, I suggest that these persons were not merely objects of assault and abuse as research has assumed; they were also leading combatants against the violence of the campaign and prophets of its demise. I introduce the concept of subversive sociality to capture the creative, cooperative, and ethical dimensions of everyday resistance.

Telling a true story about COVID-19 inequities is harder than you might think. Professor Riley will share examples from her research to demonstrate how different data leads to stories about COVID-19 inequities; and how the stories we tell about COVID-19 inequities, in turn, shape what we do about them.