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.

本研究解决了瑞士判决预测数据集中存在的偏见问题，以确保法律领域中自然语言处理模型的公平决策。通过使用偏见描述符和先进的自然语言处理技术，研究揭示了偏见对模型预测的影响，为公正的判决提供了重要的见解。

利用Facebook的整体偏见数据集分析瑞士联邦最高法院判决中的偏见：对语言模型训练的影响