Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions

Visa fullständig post



Permalänk

http://hdl.handle.net/10138/318756

Citation

Mathioudakis , M , Castillo , C , Barnabo , G & Celis , S 2020 , Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions . in PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20) . ACM , New York , pp. 440-449 , ACM/SIGAPP Symposium On Applied Computing , Brno , Czech Republic , 30/03/2020 . https://doi.org/10.1145/3341105.3373878

Titel: Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
Författare: Mathioudakis, Michael; Castillo, Carlos; Barnabo, Giorgio; Celis, Sergio
Upphovmannens organisation: Department of Computer Science
Algorithmic Data Science
Utgivare: ACM
Datum: 2020
Språk: eng
Sidantal: 10
Tillhör serie: PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20)
ISBN: 978-1-4503-6866-7
DOI: https://doi.org/10.1145/3341105.3373878
Permanenta länken (URI): http://hdl.handle.net/10138/318756
Abstrakt: We consider the problem of designing affirmative action policies for selecting the top-k candidates from a pool of applicants. We assume that for each candidate we have socio-demographic attributes and a series of variables that serve as indicators of future performance (e.g., results on standardized tests). We further assume that we have access to historical data including the actual performance of previously selected candidates. Critically, performance information is only available for candidates who were selected under some previous selection policy. In this work we assume that due to legal requirements or voluntary commitments, an organization wants to increase the presence of people from disadvantaged socio-demographic groups among the selected candidates. Hence, we seek to design an affirmative action or positive action policy. This policy has two concurrent objectives: (i) to select candidates who, given what can be learnt from historical data, are more likely to perform well, and (ii) to select candidates in a way that increases the representation of disadvantaged socio-demographic groups. Our motivating application is the design of university admission policies to bachelor's degrees. We use a causal model as a framework to describe several families of policies (changing component weights, giving bonuses, and enacting quotas), and compare them both theoretically and through extensive experimentation on a large real-world dataset containing thousands of university applicants. Our paper is the first to place the problem of affirmative-action policy design within the framework of algorithmic fairness. Our empirical results indicate that simple policies could favor the admission of disadvantaged groups without significantly compromising on the quality of accepted candidates.
Subject: 113 Computer and information sciences
Referentgranskad: Ja
Licens: unspecified
Användningsbegränsning: openAccess
Parallelpublicerad version: acceptedVersion


Filer under denna titel

Totalt antal nerladdningar: Laddar...

Filer Storlek Format Granska
mathioudakis_1144.pdf 1.196Mb PDF Granska/Öppna

Detta dokument registreras i samling:

Visa fullständig post