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

Machine learning algorithms are used in finance, medicine, and criminal justice, and therefore they can have a deep The advent of machine learning techniques has impact on society. With the recent success of AI applications made it possible to obtain predictive systems that in the private and public domain, legal professionals are now have overturned traditional legal practices. However, interested in artificial intelligence, especially since many rather than leading to systems seeking to startups disrupt the legal market space by seeking to benefit replace humans, the search for the determinants of these new AI techniques (Bex et al., 2017). in a court decision makes it possible to give a However, the arrival of these new techniques has brought better understanding of the decision mechanisms a number of ethical issues. Firstly, machine learning and carried out by the judge. By using a large amount data mining techniques are capable of exploiting personal of court decisions in matters of divorce produced and legal data that are more and more easily accessible on by French jurisdictions and by looking at the variables the Internet, leading to questions about privacy preserving, that allow to allocate an alimony or not, and or even attacks on democracy (Wylie, 2019). Secondly, to define its amount, we seek to identify if there artificial intelligence programs reason in a simplistic way, may be extralegal factors in the decisions taken but the real world is complex, especially in the legal field by the judges. From this perspective, we present which leaves a certain part to the human interpretation of an explainable AI model designed in this purpose the law and characterization of the fact. A machine learning by combining a classification with random forest program has great difficulty in dealing with the unexpected and a regression model, as a complementary tool events that happen in the real world. Intelligent system to existing decision-making scales or guidelines algorithms are black boxes that are impossible to understand, created by practitioners.