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Because preferences naturally arise and play an important role in manyreal-life decisions, they are at the backbone of various fields. In particularpreferences are increasingly used in almost all matching procedures-basedapplications. In this work we highlight the benefit of using AI insights onpreferences in a large scale application, namely the French AdmissionPost-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousandsfirst year applicants to universities. This is done automatically by matchingapplicants preferences to university seats. In practice, APB can be unable todistinguish between applicants which leads to the introduction of randomselection. This has created frustration in the French public since randomness,even used as a last mean does not fare well with the republican egalitarianprinciple. In this work, we provide a solution to this problem. We takeadvantage of recent AI Preferences Theory results to show how to enhance APB inorder to improve expressiveness of applicants preferences and reduce theirexposure to random decisions.

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