CIS Seminar: “Bias and Representation in Sociotechnical Systems”

Algorithms play a central role in our lives today, mediating our access to civic engagement, social connections, employment opportunities, news media and more. While the sociotechnical systems deploying these algorithms—search engines, social networking sites, and others—have the potential to dramatically improve human life, they also run the risk of reproducing or intensifying social inequities and tensions. In my research, I ask whether and how these systems are biased and how those biases impact users, towards the aim of building better sociotechnical systems.

Understanding sociotechnical systems and their effects requires a combination of computational and social techniques. In this talk, I will describe my work conducting algorithm audits and randomized controlled user experiments to study representation and bias, in particular my recent study of gender and racial bias in image search. By auditing gender and race in image search results for common U.S. occupations and comparing to baselines in the U.S. workforce we find that marginalized people are underrepresented relative to their workforce participation rates. When measuring people’s responses to synthetic search results in which the gender and racial composition are manipulated, however, we see that the effect of diverse image search results is complex and mediated by the user’s own identity. I will also touch on extensions of this strategy in other domains, such as my algorithm audit of partisan political media in web search results, whose results dispute allegations of partisan bias in search. I will conclude by discussing the implications of these findings for building sociotechnical systems, and directions for future research studying algorithmic bias.