Sequential Cohort Selection

TL;DR

This paper explores fair cohort selection in university admissions, comparing fixed and adaptive policies, and analyzes their fairness and meritocracy properties using population models trained on past data.

Contribution

It introduces a sequential admission framework that updates policies over time and evaluates fairness criteria in both one-shot and sequential settings.

Findings

Sequential policies can adapt to new data for improved fairness.

Fairness properties like meritocracy and group parity are analyzed.

Population models effectively guide admission decisions.

Abstract

We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before observing the actual applicant pool. In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available. This is achieved by optimizing admission policies using a population model, trained on data from previous admission cycles. We also study the fairness properties of the resulting policies in the one-shot setting, including meritocracy and group parity.