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

TL;DR

This paper designs and evaluates affirmative action policies for top-k candidate selection, specifically in university admissions, balancing performance prediction with increased representation of disadvantaged groups using a causal model framework.

Contribution

It introduces a novel framework integrating algorithmic fairness into affirmative action policy design and compares various policy types both theoretically and empirically.

Findings

Simple policies can improve disadvantaged group representation

Policies can enhance fairness without major quality loss

Empirical results validate the effectiveness of proposed methods

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.