Construction and Elicitation of a Black Box Model in the Game of Bridge

Our goal is to model expert decision processes in Bridge. To do so, we propose a methodology involving human experts, black box decision programs, and relational supervised machine learning systems. The aim is to obtain a global model for this decision process, that is both expressive and has high predictive performance. Following the success of supervised methods of the deep network family, and a growing pressure from society imposing that automated decision processes be made more transparent, a growing number of AI researchers are (re)exploring techniques to interpret, justify, or explain "black box" classifiers (referred to as the Black Box Outcome Explanation Problem [Guidotti et al., 2019]). It is a question of building, a posteriori, explicit models in symbolic languages, most often in the form of rules or deci-Daniel Braun, Colin Deheeger, Jean Pierre Desmoulins, Jean Baptiste Fantun, Swann Legras, Alexis Rimbaud, Céline Rouveirol, Henry Soldano and Véronique Ventos NukkAI, Paris, France Henry Soldano and Céline Rouveirol Université Sorbonne Paris-Nord, L.I.P.N UMR-CNRS 7030 Villetaneuse, France