应用于法律领域的决策支持系统：决策的推理与可解释性

中文摘要 AI 辅助

数字化转型的出现带来了对数字信息处理进行控制的日益增长的需求，包括在执法机构（LEAs）中。在欧盟层面，近年来出现了许多法规来控制数据处理和交换。除GDPR之外的其他文本，如《执法指令（LED）》，专门用于规范执法机构（LEAs）如何处理数据。这些法规的正式表示可以成为决策系统的一部分，这些系统支持LEAs在处理数据时遵守法规。尽管出现了许多新的形式化方法来表示法律规范和规则，但很少有提供推理机制的。此外，在关键情境（如医疗诊断或法律决策）中用于决策过程的系统不能完全自动化，其结果的可解释性对于确保用户对决策的信心至关重要。这一可解释性方面虽然至关重要，但在大多数依赖机器学习的方法中却缺失。本文描述了一个框架，用于操作来自法规的正式规则，重点关注决策的可解释性。在描述了所提出的决策支持框架的总体架构之后，本文展示了符号人工智能和SPARQL查询语言如何支持法律推理。然后，它描述了一种为推理结果生成理由的算法，并概述了当推理未导致满意结论时应遵循的程序。我们特别关注一种基于决策树的方法，以确定应向用户请求哪些额外信息。

英文摘要

The emergence of the digital transition brought an increasing need to control the processing of digital information, including in Law Enforcement Agencies (LEAs). At the EU level, in recent years, many regulations have emerged to control data processing and exchange. Texts other than the GDPR, such as the ''Law Enforcement Directive (LED)'', appeared to regulate specifically how Law Enforcement Agencies (LEAs) could process data. A formal representation of these regulations can be part of decision systems that support LEAs in processing data in compliance with the regulations. Although many new formalisms have emerged to represent legal norms and rules, few are provided with a reasoning mechanism. Furthermore, systems used in decision-making processes in critical contexts such as medical diagnoses or legal decisions cannot be fully automated, and the explainability of their results is essential to ensure user confidence in decisions. This explainability aspect, while crucial, is lacking in most modern approaches that rely on machine learning. This paper describes a framework to operate formal rules from regulations, by focusing on explainability of the decision. After describing the general architecture of the proposed decision support framework, the paper showcases how symbolic AI and the SPARQL query language can support legal reasoning. It then describes an algorithm to generate a justification for the reasoning results, and outlines the procedure to be followed when the reasoning does not lead to a satisfactory conclusion. We notably focus on a method based on decision trees to determine what additional information to request from the user.

发表机构

• CNRS（法国国家科学研究中心）
• IRIT（图卢兹计算机研究所）

机构由 AI 辅助整理，请以论文原文为准。