赡养费判决预测：避免法官判决中的法外因素和不可理解的AI模型
Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models
摘要：
机器学习技术的出现使得获得颠覆传统法律实践的预测系统成为可能。然而，寻找法院判决中的决定因素并不会导致试图取代人类的系统，而是使人们有可能更好地理解法官执行的判决机制。通过使用法国司法管辖区在离婚问题上的大量法院判决，并通过查看允许分配赡养费或不允许分配赡养费的变量，并确定其金额，我们试图确定法官所作的决定中是否可能存在法律外因素。从这个角度来看，我们提出了一个可解释的人工智能模型，通过将分类与随机森林和回归模型相结合，作为现有决策尺度或从业者创建的指南的补充工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
