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

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dc.contributor.author Mathioudakis, Michael
dc.contributor.author Castillo, Carlos
dc.contributor.author Barnabo, Giorgio
dc.contributor.author Celis, Sergio
dc.date.accessioned 2020-08-28T12:27:01Z
dc.date.available 2020-08-28T12:27:01Z
dc.date.issued 2020
dc.identifier.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
dc.identifier.citation conference
dc.identifier.other PURE: 160891342
dc.identifier.other PURE UUID: 2f1f73d5-798d-462e-9a5b-d20841b7ecf7
dc.identifier.other ORCID: /0000-0003-0074-3966/work/79521109
dc.identifier.other WOS: 000569720900063
dc.identifier.uri http://hdl.handle.net/10138/318756
dc.description.abstract 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. en
dc.format.extent 10
dc.language.iso eng
dc.publisher ACM
dc.relation.ispartof PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20)
dc.relation.isversionof 978-1-4503-6866-7
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.contributor.organization Algorithmic Data Science
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1145/3341105.3373878
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion
dc.identifier.url https://arxiv.org/abs/1905.09947

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