Towards Data-Driven Affirmative Action Policies under Uncertainty

Show full item record



Permalink

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

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 >

Title: Towards Data-Driven Affirmative Action Policies under Uncertainty
Author: Hertweck, Corinna Isabell; Castillo, Carlos; Mathioudakis, Michael
Contributor organization: Department of Computer Science
Algorithmic Data Science
Date: 2020
Language: eng
URI: http://hdl.handle.net/10138/333824
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.
Subject: 113 Computer and information sciences
Peer reviewed: Yes
Rights: unspecified
Usage restriction: openAccess
Self-archived version: acceptedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
Designing_Affir ... er_Uncertainty_FATED_1.pdf 542.0Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record