质疑案件判决摘要中的偏见：法律数据集还是大语言模型？
Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?
AI总结：
本文通过分析法律数据集和大语言模型生成的案件判决摘要，揭示其在性别、种族、涉女性犯罪、国家及宗教关键词上的偏见，为理解法律技术中的公平性问题提供证据。
摘要：
法律数据集的演变与大语言模型（LLMs）的出现已经显著改变了法律领域，尤其是在案件判决摘要的生成方面。然而，一个关键问题随之而来，即这些摘要中可能嵌入的偏见。本研究审视了由法律数据集和大语言模型生成的案件判决摘要中存在的偏见。该研究旨在分析偏见对法律决策的影响。通过质疑这些摘要中偏见的准确性、公平性及其影响，本研究有助于更好地理解技术在法律语境中的作用，以及对全球司法体系的影响。在本研究中，我们调查了与性别相关关键词、种族相关关键词、针对女性犯罪相关关键词、国家名称和宗教关键词有关的偏见。研究展示了大语言模型和预训练抽象式摘要模型所生成输出中存在偏见的有趣证据。这些偏见背后的原因需要进一步研究。
英文摘要：
The evolution of legal datasets and the advent of large language models (LLMs) have significantly transformed the legal field, particularly in the generation of case judgment summaries. However, a critical concern arises regarding the potential biases embedded within these summaries. This study scrutinizes the biases present in case judgment summaries produced by legal datasets and large language models. The research aims to analyze the impact of biases on legal decision making. By interrogating the accuracy, fairness, and implications of biases in these summaries, this study contributes to a better understanding of the role of technology in legal contexts and the implications for justice systems worldwide. In this study, we investigate biases wrt Gender-related keywords, Race-related keywords, Keywords related to crime against women, Country names and religious keywords. The study shows interesting evidences of biases in the outputs generated by the large language models and pre-trained abstractive summarization models. The reasoning behind these biases needs further studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
