Bio: Faidra Monachou is a final-year Ph.D. candidate in Operations Research at the Department of Management Science and Engineering at Stanford University. She is interested in market and information design, with a particular focus on the interplay between policy design and discrimination in education and labor. Faidra’s research has been supported by various scholarships and fellowships from Stanford Data Science, Stanford HAI, Google, and other organizations. She won the Best Paper with a Student Presenter Award at ACM EAAMO’21. She co-chaired the 2020 Mechanism Design for Social Good workshop and co-organized the 2021 Stanford Data Science for Social Good program. Faidra received her undergraduate degree in Electrical and Computer Engineering from the National Technical University of Athens in Greece.
Talk Title: Discrimination, Diversity, and Information in Selection Problems
Talk Abstract: Despite the large empirical literature on disparities in college admissions, our theoretical understanding is limited. In this talk, I will introduce a theoretical framework to study how a decision-maker concerned with both merit and diversity, selects candidate students under imperfect information, limited capacity, and legal constraints. Motivated by recent decisions to drop standardized testing in admissions, we apply this framework to study how information differences lead to disparities across equally skilled groups and quantify the trade-off between information and access in test-free and test-based policies with and without affirmative action. Using application and transcript data from the University of Texas at Austin, we illustrate that there exist practical settings where dropping standardized testing improves or worsens both merit and diversity. Furthermore, we extend this model to demonstrate how privilege differences lead to intra-group disparities and establish that the direction of discrimination at the observable level may differ from the unobservable level. We compare common policies used in practice and take an optimization approach to design an optimal policy under legal constraints.