Towards Data-Driven Affirmative Action Policies under Uncertainty

Corinna Isabell Hertweck, Carlos Castillo, Michael Mathioudakis

Research output: Conference materialsPaperpeer-review


In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. We consider affirmative action policies that seek to increase the number of admitted applicants from underrepresented groups. Since such a policy has to be announced before the start of the application period, there is uncertainty about the score distribution of the students applying to each program. This poses a difficult challenge for policy makers. We explore the possibility of using a predictive model trained on historical data to help optimize the parameters of such policies.
Original languageEnglish
Publication statusPublished - 2020
MoE publication typeNot Eligible
Event Educational Data Mining Workshops 2020 -
Duration: 10 Jul 2020 → …


Workshop Educational Data Mining Workshops 2020
Abbreviated titleFATED 2020
Period10/07/2020 → …
Internet address

Fields of Science

  • 113 Computer and information sciences

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