Fair and Useful Cohort Selection
This work addresses fairness preservation in algorithmic decision-making for cohort selection, offering practical solutions with utility guarantees, though it is incremental as it builds on prior fairness composition frameworks.
The paper tackles the fair cohort selection problem by designing algorithms that preserve fairness while maximizing utility under two new utility notions, providing optimal or near-optimal polynomial-time solutions for both offline and online settings.
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.