The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

翻译：法官与案件比例失衡对司法系统造成沉重负担，表现为大量积压案件与持续新增案件并存。为解决此问题并加快司法进程，基于事实证据与历史判例自动生成案件结果的系统具有重要意义。本研究聚焦于开发基于图神经网络的模型，以解决法律判决预测（LJP）问题，通过识别司法案件中固有的图结构特性，将其转化为二元节点分类任务。我们探索了多种嵌入作为模型特征，同时添加并剪枝时间节点与司法行为节点等以评估模型性能。研究过程中充分考虑了预测公平性的伦理维度，包括性别与姓名偏见。此外还开展了链接预测任务，评估模型预测两个指定节点间关联的能力。通过利用图神经网络能力并融入公平性分析，本研究旨在为简化审判流程、提升司法效率、构建更公平的法律环境提供见解，最终缓解案件积压带来的压力。采用XLNet预训练嵌入作为特征的最优模型在LJP任务上取得了75%的宏F1分数；在链接预测任务中，相同特征集表现最佳，ROC值超过80%。