Discrimination, diversity, and information in college admissions policies
Reducing disparities and improving diversity and access is a topic of growing importance in decision-making settings. Motivated by documented disparities in college admissions and recent policy debates in higher education, we introduce a theoretical framework to study how a decision-maker, concerned with both merit and diversity, selects candidates under imperfect information, limited capacity, and possibly legal constraints. We apply this framework to capture how disparities both across and within social groups can arise even in the absence of prejudice and study the tradeoffs of admissions policies used in practice to address these disparities (dropping standardized testing, top percent rule). Moreover, we take an optimization-based approach and incorporate fairness metrics to a generalized optimization objective with legal constraints. We find that the optimal selection policy takes the form of a middle-tier lottery.
Faidra Monachou is a Ph.D. candidate in management science and engineering at Stanford University, advised by Professor Itai Ashlagi. Her research centers around market and policy design questions, with a particular focus on education and social equity. She was awarded the Google Women Techmakers Scholarship, won the Best Paper with a Student Presenter Award at ACM EAAMO'21 and was recently selected as a Rising Star in EECS (MIT) and a Rising Star in Data Science (University of Chicago). Faidra received her undergraduate degree in electrical and computer engineering from the National Technical University of Athens in Greece.