James Chu

Session: Afternoon Scientific Session - Bias, Fairness, and Inequality in an Algorithmic Age
Title: Algorithmic Reinterpretations: College Rankings and Socioeconomic Self-Sorting
Abstract: Public metrics like college rankings are algorithms that incorporate multiple inputs into single quantities, and a recurring criticism is that they impose excessive uniformity in evaluations. A less explored possibility is that single quantities enable users to draw diverse algorithmic reinterpretations that reflect their own background. I investigate the inequality implications of this possibility in the context of educational rankings. In conjoint experiment conducted among a diverse sample of U.S. adults (n=1,968), I show that college rankings are stronger signals exclusivity, academic rigor, safety, and stress for those from higher socioeconomic status (SES) backgrounds, while serving as stronger signals of exclusion (unwelcoming) for those from lower SES backgrounds. In a second experiment among a diverse sample of U.S. adolescents (n=800), I find that first-generation students perceive rankings as a stronger signal for college cost than their more advantaged peers, with no corresponding change in perceived financial aid. These cost differences partially explain why lower SES students prefer not to attend higher prestige colleges. In the case of college rankings, algorithms produce quantities that are differentially reinterpreted along SES cleavages, likely contributing to self-sorting where lower SES students prefer colleges of lower prestige, and vice versa.

James Chu studies economic and organizational sociology, social stratification, and political polarization. His primary line of research investigates how status is defined and allocated among social actors, and how varying ways of organizing status competitions translate to different patterns of inequality and conflict. James Chu's website.