Fair and Useful Cohort Selection

TL;DR

This paper develops algorithms for fair cohort selection that maintain fairness and maximize utility, applicable in both offline and online scenarios, addressing challenges in fair algorithm composition.

Contribution

It introduces new utility notions and provides optimal or near-optimal polynomial-time algorithms for fair cohort selection in offline and online settings.

Findings

Algorithms preserve fairness under composition.

Utility maximization improves cohort quality.

Effective solutions for both offline and online scenarios.

Abstract

A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness. To study fairness under composition, Dwork and Ilvento introduced an archetypal problem called fair-cohort-selection problem, where a single fair classifier is composed with itself to select a group of candidates of a given size, and proposed a solution to this problem. In this work we design algorithms for selecting cohorts that not only preserve fairness, but also maximize the utility of the selected cohort under two notions of utility that we introduce and motivate. We give optimal (or approximately optimal) polynomial-time algorithms for this problem in both an offline setting, and an online setting where candidates arrive one at a time and are classified as they arrive.