Natural Language Processing (NLP) is vital for computers to process and respond accurately to human language. However, biases in training data can introduce unfairness, especially in predicting legal judgment. This study focuses on analyzing biases within the Swiss Judgment Prediction Dataset (SJP-Dataset). Our aim is to ensure unbiased factual descriptions essential for fair decision making by NLP models in legal contexts. We analyze the dataset using social bias descriptors from the Holistic Bias dataset and employ advanced NLP techniques, including attention visualization, to explore the impact of dispreferred descriptors on model predictions. The study identifies biases and examines their influence on model behavior. Challenges include dataset imbalance and token limits affecting model performance.

翻译：自然语言处理（NLP）对于计算机准确处理和响应人类语言至关重要。然而，训练数据中的偏见可能引入不公平性，这在法律判决预测中尤为突出。本研究重点分析瑞士判决预测数据集（SJP-Dataset）中存在的偏见。我们的目标是确保NLP模型在法律语境中做出公平决策所需的无偏见事实描述。我们使用整体偏见数据集中的社会偏见描述符对该数据集进行分析，并采用包括注意力可视化在内的先进NLP技术，以探讨非优选描述符对模型预测的影响。本研究识别了数据集中存在的偏见，并检验了这些偏见对模型行为的影响。面临的挑战包括数据集不平衡以及影响模型性能的标记长度限制。