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

• URL: http://arxiv.org/abs/2007.04824v1
• Date: Thu, 9 Jul 2020 14:14:20 GMT
• Title: Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models
• Authors: Fabrice Muhlenbach, Long Nguyen Phuoc and Isabelle Sayn
• Abstract summary: We present an explainable AI model designed in this purpose by combining a classification with random forest and a regression model. By using a large amount of court decisions in matters of divorce produced by French jurisdictions, we seek to identify if there may be extra-legal factors in the decisions taken by the judges.
• Score: 0.02578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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