Data derived from the realm of the social sciences is often produced in
digital text form, which motivates its use as a source for natural language
processing methods. Researchers and practitioners have developed and relied on
artificial intelligence techniques to collect, process, and analyze documents
in the legal field, especially for tasks such as text summarization and
classification. While increasing procedural efficiency is often the primary
motivation behind natural language processing in the field, several works have
proposed solutions for human rights-related issues, such as assessment of
public policy and institutional social settings. One such issue is the presence
of gender biases in court decisions, which has been largely studied in social
sciences fields; biased institutional responses to gender-based violence are a
violation of international human rights dispositions since they prevent gender
minorities from accessing rights and hamper their dignity. Natural language
processing-based approaches can help detect these biases on a larger scale.
Still, the development and use of such tools require researchers and
practitioners to be mindful of legal and ethical aspects concerning data
sharing and use, reproducibility, domain expertise, and value-charged choices.
In this work, we (a) present an experimental framework developed to
automatically detect gender biases in court decisions issued in Brazilian
Portuguese and (b) describe and elaborate on features we identify to be
critical in such a technology, given its proposed use as a support tool for
research and assessment of court~activity.

该研究提出了一个实验框架，用于自动检测巴西葡萄牙语法院判决中的性别偏见，并描述了这项技术的关键特征，以支持研究和评估法院活动。

法庭裁决中的性别偏见检测：巴西案例研究

Gender Bias Detection in Court Decisions: A Brazilian Case Study

To undertake computational research of the law, efficiently identifying
datasets of court decisions that relate to a specific legal issue is a crucial
yet challenging endeavour. This study addresses the gap in the literature
working with large legal corpora about how to isolate cases, in our case
summary judgments, from a large corpus of UK court decisions. We introduce a
comparative analysis of two computational methods: (1) a traditional natural
language processing-based approach leveraging expert-generated keywords and
logical operators and (2) an innovative application of the Claude 2 large
language model to classify cases based on content-specific prompts. We use the
Cambridge Law Corpus of 356,011 UK court decisions and determine that the large
language model achieves a weighted F1 score of 0.94 versus 0.78 for keywords.
Despite iterative refinement, the search logic based on keywords fails to
capture nuances in legal language. We identify and extract 3,102 summary
judgment cases, enabling us to map their distribution across various UK courts
over a temporal span. The paper marks a pioneering step in employing advanced
natural language processing to tackle core legal research tasks, demonstrating
how these technologies can bridge systemic gaps and enhance the accessibility
of legal information. We share the extracted dataset metrics to support further
research on summary judgments.

本文使用自然语言处理技术，比较了基于关键词和逻辑运算符的传统方法与基于 Claude 2 大型语言模型的创新方法，在英国法院裁决案例的大语料库中提取总结性判决案例。结果表明，大语言模型的加权 F1 得分为 0.94，而关键词法的得分仅为 0.78，说明大语言模型在捕捉法律语言中的细微差别方面更加有效。本文展示了先进自然语言处理技术在核心法律研究任务中的应用，并且阐明了这些技术如何填补系统性差距并提升法律信息的可获取性。同时，我们分享了提取的数据集度量，以支持进一步的总结性判决研究。

LLM 与律师：在大型英国案例法律数据库中识别摘要判决的子集

LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK  Case Law Dataset

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.

通过使用大量的法庭离婚案例并观察允许分配抚养费的变量，本研究通过结合分类和随机森林回归模型，提供了一个可解释的人工智能模型，旨在作为现有决策工具的补充，以更好地理解法官所做的决策机制。