Event invitation: “Who Decides What Is Fair and Do They Understand the Trade-Offs? Measuring Public Judgments of Algorithmic Decision-Making” by Malik Boykin

Who Decides What Is Fair and Do They Understand the Trade-Offs? Measuring Public Judgments of Algorithmic Decision-Making

Artificial intelligence is increasingly embedded in the institutions that structure everyday life. Systems now influence decisions about hiring, lending, healthcare, and criminal justice. Yet while these technologies shape social outcomes, the public rarely has an opportunity to meaningfully evaluate the values embedded within them. A central challenge is that there is no single definition of algorithmic fairness. In fact, many commonly proposed fairness metrics are mathematically incompatible. Choosing one approach over another, therefore, reflects not only a technical decision but a moral and institutional one. In this talk, I introduce a new approach for studying how people reason about these trade-offs. Using interactive data visualizations, participants explore machine learning models with varying fairness properties and indicate which outcomes they consider most fair. The method allows researchers to observe how individuals translate moral intuitions into concrete algorithmic preferences. Results from a preregistered validation study show that conceptual understanding of machine learning strongly predicts how people interpret fairness trade-offs. Individuals who better understand the underlying concepts are more consistent in their judgments and more likely to favor fairness criteria that equalize certain forms of algorithmic harm. At the same time, greater understanding is associated with lower levels of trust in machine learning systems. Together, these findings suggest that algorithmic literacy plays an important role in shaping how people evaluate AI-mediated decisions. More broadly, this work introduces a psychometrically validated framework for measuring public judgments of algorithmic fairness and offers a pathway to incorporate public values into debates on AI governance and institutional accountability.

Bio: Dr. C. Malik Boykin is a social psychologist whose work examines how people understand fairness, bias, and identity in the institutions that shape everyday life. His research spans intergroup relations, prejudice, mentorship, and the growing role of artificial intelligence in decision-making. In an interdisciplinary collaboration, he studies how people interpret the trade-offs embedded in algorithmic systems and what those judgments reveal about social values. Boykin is an Assistant Professor at Brown University. Before joining the faculty, he served as a Presidential Postdoctoral Fellow in the Department of Cognitive, Linguistic, and Psychological Sciences. His research combines approaches from social psychology, data science, and psychometrics to examine how people evaluate algorithmic decisions. Questions of trust, legitimacy, and perceived fairness are threaded throughout this work. Boykin’s research has been supported by organizations including the Ford Foundation Fellowship Program, the National Science Foundation, the Greater Good Science Center, and the Society for Multivariate Experimental Psychology. Outside academia, Boykin is also a hip-hop artist who performs under the name, Malik Starx. In classrooms, research talks, and live performances, he moves between scholarship and rhythm to spark conversations about persistence, identity, and the social consequences of emerging technologies.