用模态逻辑形式化法律判例推理，支持时间与法院层级的冲突解决
A Modal Logic for Temporal and Jurisdictional Classifier Models
- 构建融合时间维度与法院层级的判例逻辑模型
- 可形式化处理判例间的冲突与优先级关系
- 适合法律AI验证与可解释性研究者参考
基于逻辑的模型可用于构建用于法律领域机器学习分类器的验证工具。机器学习分类器根据以往案例预测新案件结果，从而实现一种类比推理（Case-Based Reasoning, CBR）。本文提出一种针对分类器的模态逻辑，旨在形式化表达法律中的判例推理。通过引入案件的时间维度以及法律体系中法院的层级结构，该逻辑模型能够形式化处理判例间的冲突解决原则。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。