Title: Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models

Abstract: The advent of machine learning techniques has made it possible to obtain predictive systems that have overturned traditional legal practices. However, rather than leading to systems seeking to replace humans, the search for the determinants in a court decision makes it possible to give a better understanding of the decision mechanisms carried out by the judge. By using a large amount of court decisions in matters of divorce produced by French jurisdictions and by looking at the variables that allow to allocate an alimony or not, and to define its amount, we seek to identify if there may be extra-legal factors in the decisions taken by the judges. From this perspective, we present an explainable AI model designed in this purpose by combining a classification with random forest and a regression model, as a complementary tool to existing decision-making scales or guidelines created by practitioners.

Authors: Fabrice Muhlenbach, Long Nguyen Phuoc, and Isabelle Sayn

Bio of the main author: Fabrice Muhlenbach is an associate professor at the University of Lyon (Lab. Hubert Curien, UMR CNRS 5516, UJM Saint-Etienne, France). He studied in Strasbourg, Lyon and Paris and has a background in computer science, psychology and cognitive science. After a PhD on the field of data mining, he became interested in the subject of recommender systems, and he worked in particular on the problem of the lack of diversity in machine learning-based systems. He focuses today on the ethical issues associated with the introduction of artificial intelligence into society.