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.

翻译：法律数据集的发展与大语言模型（LLMs）的出现显著改变了法律领域，尤其在案件判决摘要生成方面。然而，这些摘要中潜在的偏见问题引发了关键性关注。本研究深入审视法律数据集与大语言模型生成的案件判决摘要中存在的偏见，旨在分析偏见对法律决策的影响。通过探究这些摘要的准确性、公平性及其偏见的影响，本研究有助于更深入地理解技术在法律语境中的作用及其对全球司法系统的启示。我们重点考察了与性别相关关键词、种族相关关键词、针对女性犯罪关键词、国家名称及宗教关键词相关的偏见。研究揭示了在大型语言模型和预训练摘要生成模型的输出中存在的明显偏见证据，这些偏见背后的原因仍需进一步研究。