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. In my research, I ask whether and how these systems are biased, and how those biases impact users.
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, focusing on 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 conclude by discussing the implications of these findings for building sociotechnical systems, and directions for future research studying algorithmic bias.
Danaë Metaxa (they/she) is a PhD candidate in Computer Science at Stanford University, advised by James Landay and Jeff Hancock. A member of the Human-Computer Interaction group, Danaë’s research interests focus on building and understanding sociotechnical systems and their effects on users in domains like employment and politics. Danaë has been a pre-doctoral scholar with Stanford’s Program on Democracy and the Internet, a fellow with the McCoy Center for Ethics in Society, and the winner of an NSF Graduate Research Fellowship.