Preference Reasoning in Matching Procedures: Application to the Admission Post-Baccalaureat Platform

TL;DR

This paper demonstrates how AI preferences theory can improve the French university admission platform by making preference-based matching more expressive and reducing reliance on random selection, thus aligning better with egalitarian principles.

Contribution

It introduces AI insights into preference modeling to enhance the APB matching process, reducing randomness and increasing fairness in university admissions.

Findings

Enhanced preference expressiveness in APB

Reduced reliance on random selection

Improved fairness in matching outcomes

Abstract

Because preferences naturally arise and play an important role in many real-life decisions, they are at the backbone of various fields. In particular preferences are increasingly used in almost all matching procedures-based applications. In this work we highlight the benefit of using AI insights on preferences in a large scale application, namely the French Admission Post-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousands first year applicants to universities. This is done automatically by matching applicants preferences to university seats. In practice, APB can be unable to distinguish between applicants which leads to the introduction of random selection. This has created frustration in the French public since randomness, even used as a last mean does not fare well with the republican egalitarian principle. In this work, we provide a solution to this problem. We take advantage of recent AI Preferences Theory results to show how to enhance APB in order to improve expressiveness of applicants preferences and reduce their exposure to random decisions.