Towards Data-Driven Affirmative Action Policies under Uncertainty

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dc.contributor.author Hertweck, Corinna Isabell
dc.contributor.author Castillo, Carlos
dc.contributor.author Mathioudakis, Michael
dc.date.accessioned 2021-08-31T12:55:01Z
dc.date.available 2021-08-31T12:55:01Z
dc.date.issued 2020
dc.identifier.citation 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 >
dc.identifier.citation workshop
dc.identifier.other PURE: 143035378
dc.identifier.other PURE UUID: 635a8579-74cf-427d-b8fa-6f9601b5b80f
dc.identifier.other ORCID: /0000-0003-0074-3966/work/99272926
dc.identifier.uri http://hdl.handle.net/10138/333824
dc.description.abstract 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. en
dc.language.iso eng
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Towards Data-Driven Affirmative Action Policies under Uncertainty en
dc.type Paper
dc.contributor.organization Department of Computer Science
dc.contributor.organization Algorithmic Data Science
dc.description.reviewstatus Peer reviewed
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion
dc.identifier.url https://arxiv.org/abs/2007.01202

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