Fair and Optimal Cohort Selection for Linear Utilities

TL;DR

This paper introduces algorithms for fair and optimal cohort selection that maximize linear utility, applicable in both offline and online settings, addressing fairness in algorithmic decision-making.

Contribution

It presents the first polynomial-time algorithms for fair cohort selection with linear utilities in both offline and online scenarios.

Findings

Algorithms achieve approximate optimality.

Effective in both offline and online settings.

Addresses fairness in cohort selection with linear utilities.

Abstract

The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms. While there are many compelling notions of individual fairness, beginning with the work of Dwork et al., these notions typically do not satisfy desirable composition properties. To this end, Dwork and Ilvento introduced the fair cohort selection problem, which captures a specific application where a single fair classifier is composed with itself to pick a group of candidates of size exactly $k$. In this work we introduce a specific instance of cohort selection where the goal is to choose a cohort maximizing a linear utility function. We give approximately optimal polynomial-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.