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We consider the problem of designing affirmative action policies forselecting the top-k candidates from a pool of applicants. We assume that foreach candidate we have socio-demographic attributes and a series of variablesthat serve as indicators of future performance (e.g., results on standardizedtests). We further assume that we have access to historical data including theactual performance of previously selected candidates. Critically, performanceinformation is only available for candidates who were selected under someprevious selection policy.  In this work we assume that due to legal requirements or voluntarycommitments, an organization wants to increase the presence of people fromdisadvantaged socio-demographic groups among the selected candidates. Hence, weseek to design an affirmative action or positive action policy. This policy hastwo concurrent objectives: (i) to select candidates who, given what can belearnt from historical data, are more likely to perform well, and (ii) toselect candidates in a way that increases the representation of disadvantagedsocio-demographic groups.  Our motivating application is the design of university admission policies tobachelor's degrees. We use a causal model as a framework to describe severalfamilies of policies (changing component weights, giving bonuses, and enactingquotas), and compare them both theoretically and through extensiveexperimentation on a large real-world dataset containing thousands ofuniversity applicants. Our paper is the first to place the problem ofaffirmative-action policy design within the framework of algorithmic fairness.Our empirical results indicate that simple policies could favor the admissionof disadvantaged groups without significantly compromising on the quality ofaccepted candidates.

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