Subfield Prestige and Gender Inequality among U.S. Computing Faculty

The composition of the academic workforce thus shapes what advances are made and who benefits from them,20,21 in part because demographic diversity in science is known to accelerate innovation and improve problem solving.17,31 Despite a continued emphasis on broadening participation, women faculty in the U.S. remain underrepresented relative to women's share of the U.S. population by more than a factor of two, and Black, Hispanic, and Native faculty by more than a factor of five.37,40 Women's underrepresentation among computing researchers also persists internationally. For example, women are estimated to comprise less than 10% of contributors to international computer science journals.25 On one hand, there are generational problems, in which faculty diversity changes slowly because it takes many years for diversity increases at the earliest stages of training to propagate up to more senior levels.16 On the other hand, there are structural and social climate problems in the U.S.,1 in which members of underrepresented groups who aspire to or have a faculty career are pushed or pulled out of the community, which may counteract efforts to address generational problems. In concert, these two effects may lead to a persistent overrepresentation of majority groups5 despite efforts to the contrary. We consider a third class of problem, which exists because most faculty are hired via searches that focus on a particular subfield of computing--for example, artificial intelligence (AI). As a result, field-level demographic dynamics such as gender, racial, and socioeconomic representation are in fact driven by diversity differences across computing's subfields and the representation of those subfields among the suppliers of future faculty.8 For example, faculty searches in subfields with fewer women than other subfields are less likely to increase a department's gender diversity.