Machine learning shows promise in predicting the outcome of legal cases, but most research has concentrated on civil law cases rather than case law systems. We identified two unique challenges in making legal case outcome predictions with case law. First, it is crucial to identify relevant precedent cases that serve as fundamental evidence for judges during decision-making. Second, it is necessary to consider the evolution of legal principles over time, as early cases may adhere to different legal contexts. In this paper, we proposed a new framework named PILOT (PredictIng Legal case OuTcome) for case outcome prediction. It comprises two modules for relevant case retrieval and temporal pattern handling, respectively. To benchmark the performance of existing legal case outcome prediction models, we curated a dataset from a large-scale case law database. We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.

翻译：摘要：机器学习在预测法律案件结果方面展现出潜力，但多数研究集中于大陆法系案件而非判例法体系。我们识别出在判例法中进行法律案件结果预测的两项独特挑战。首先，关键在于识别相关先例案件，这些案件是法官决策过程中的基础证据。其次，必须考虑法律原则随时间的演变，因为早期案件可能遵循不同的法律语境。本文提出了一种名为PILOT（PredictIng Legal case OuTcome）的新框架，用于案件结果预测。该框架包含两个模块，分别用于相关案件检索和时间模式处理。为评估现有法律案件结果预测模型的性能，我们从大规模判例法数据库中整理了一个数据集。我们证明了准确识别先例案件并缓解时间偏移对判例法预测的重要性，因为我们的方法相较于专注于大陆法系案件结果预测的先前方法表现出显著改进。