Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system.

翻译：基于逻辑的模型可用于构建法律领域机器学习分类器的验证工具。机器学习分类器基于先前案例预测新案件的结果，从而执行一种基于案例的推理（CBR）。本文提出一种分类器的模态逻辑，旨在形式化地刻画法律领域的CBR。我们通过将案件的时间维度与法律体系中法院的层级结构引入逻辑系统，整合了解决判例间冲突的原则。