(University of Washington)
Negative Interest Stereotypes and Masculine Defaults: Understanding and Remedying Two Forms of Gender Bias
Women and girls continue to be underrepresented in computer science and engineering, despite many efforts by universities, nonprofits, corporations, and government to close these gender
gaps (National Science Foundation, 2020). A great deal of work has focused on gender biases against girls and women as a prominent source of these gender disparities. In the current talk, I present two understudied forms of gender biases that may explain current disparities in male-dominated fields. First, I document the presence and negative consequences of stereotypes that depict girls as less interested than boys in computer science and engineering. Second, I present work on masculine defaults, a form of bias in which characteristics or behaviors associated with the male gender role are valued, rewarded, or regarded as standard aspects of a culture. Increasing girls’ and women’s participation in majority-male fields may involve identifying and counteracting these forms of gender biases.