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

Michael Mathioudakis, Carlos Castillo, Giorgio Barnabo, Sergio Celis

Research output: Conference materialsPaper

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.
Original languageEnglish
Publication statusPublished - 2020
MoE publication typeNot Eligible
EventThe 35th ACM/SIGAPP Symposium On Applied Computing - Brno, Czech Republic
Duration: 30 Mar 20203 Apr 2020
http://www.sigapp.org/sac/sac2020/

Conference

ConferenceThe 35th ACM/SIGAPP Symposium On Applied Computing
Abbreviated titleSAC2020
CountryCzech Republic
CityBrno
Period30/03/202003/04/2020
Internet address

Cite this

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. Paper presented at The 35th ACM/SIGAPP Symposium On Applied Computing, Brno, Czech Republic.