Towards Data-Driven Affirmative Action Policies under Uncertainty

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http://hdl.handle.net/10138/333824

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Hertweck , C I , Castillo , C & Mathioudakis , M 2020 , ' Towards Data-Driven Affirmative Action Policies under Uncertainty ' , Paper presented at Educational Data Mining Workshops 2020 , 10/07/2020 . < https://arxiv.org/abs/2007.01202 >

Titel: Towards Data-Driven Affirmative Action Policies under Uncertainty
Författare: Hertweck, Corinna Isabell; Castillo, Carlos; Mathioudakis, Michael
Upphovmannens organisation: Department of Computer Science
Algorithmic Data Science
Datum: 2020
Språk: eng
Permanenta länken (URI): http://hdl.handle.net/10138/333824
Abstrakt: 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.
Subject: 113 Computer and information sciences
Referentgranskad: Ja
Licens: unspecified
Användningsbegränsning: openAccess
Parallelpublicerad version: acceptedVersion


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