用社交偏见数据集分析瑞士判例中的偏见，提升法律AI公平性。
Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's Holistic Bias Dataset: Implications for Language Model Training
- 引入Facebook Holistic Bias数据集，识别判例文本中的社会偏见
- 发现特定词汇显著影响模型判决预测，存在偏差风险
- 适合法律AI、NLP伦理研究者参考
自然语言处理对计算机理解人类语言至关重要，但训练数据中的偏见可能引发不公平，尤其在法律判决预测中。本研究聚焦瑞士判决预测数据集（SJP-Dataset），旨在确保NLP模型在法律场景下作出无偏的事实描述。我们利用Facebook Holistic Bias数据集中的社会偏见标签，结合注意力可视化等先进NLP技术，探究非偏好表述对模型预测的影响。研究揭示了数据集中的偏见及其对模型行为的作用机制。面临的主要挑战包括数据分布不均和分词长度限制对模型性能的制约。
原文摘要 · Abstract (English)
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.
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