Sequential Cohort Selection
This addresses fairness in admissions for universities, but appears incremental as it builds on existing cohort selection and fairness concepts.
The paper tackles the problem of fair cohort selection in university admissions by comparing one-shot and sequential settings, where policies are fixed in advance or updated with new data, and analyzes fairness properties like meritocracy and group parity.
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.