  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.
