Artificial Intelligence techniques such as Machine Learning (ML) have not
been exploited to their maximum potential in the legal domain. This has been
partially due to the insufficient explanations they provided about their
decisions. Automatic expert systems with explanatory capabilities can be
specially useful when legal practitioners search jurisprudence to gather
contextual knowledge for their cases. Therefore, we propose a hybrid system
that applies ML for multi-label classification of judgements (sentences) and
visual and natural language descriptions for explanation purposes, boosted by
Natural Language Processing techniques and deep legal reasoning to identify the
entities, such as the parties, involved. We are not aware of any prior work on
automatic multi-label classification of legal judgements also providing natural
language explanations to the end-users with comparable overall quality. Our
solution achieves over 85 % micro precision on a labelled data set annotated by
legal experts. This endorses its interest to relieve human experts from
monotonous labour-intensive legal classification tasks.

提出了一个将人工智能、机器学习、自然语言处理与深度法律推理结合的混合系统，用于法律判决的多标签分类和解释，通过提供自然语言解释来减轻法律专家的重复劳动密集型法律分类任务。

西班牙法律判决的可解释机器学习多标签分类

Explainable machine learning multi-label classification of Spanish legal  judgements

This paper presents a comprehensive study on the evaluation of explanatory
capabilities of machine learning models, with a focus on Decision Trees, Random
Forest and XGBoost models using a pancreatic cancer dataset. We use
Human-in-the-Loop related techniques and medical guidelines as a source of
domain knowledge to establish the importance of the different features that are
relevant to establish a pancreatic cancer treatment. These features are not
only used as a dimensionality reduction approach for the machine learning
models, but also as way to evaluate the explainability capabilities of the
different models using agnostic and non-agnostic explainability techniques. To
facilitate interpretation of explanatory results, we propose the use of
similarity measures such as the Weighted Jaccard Similarity coefficient. The
goal is to not only select the best performing model but also the one that can
best explain its conclusions and aligns with human domain knowledge.

这篇论文综合研究了决策树、随机森林和 XGBoost 模型在胰腺癌数据集上的解释能力评估，并使用人机协同相关技术和医学指南作为领域知识的来源，以确定与胰腺癌治疗相关的不同特征的重要性。同时，这些特征不仅用于机器学习模型的降维，还用于使用无偏和非无偏解释技术评估不同模型的可解释性能力。为了便于解释结果的理解，提出了使用加权杰卡德相似系数等相似度度量方法。研究目标不仅是选择表现最佳的模型，还是能够最好地解释其结论并与人类领域知识相一致的模型。