Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's Holistic Bias Dataset: Implications for Language Model Training

Sabine Wehnert, Muhammet Ertas, Ernesto William De Luca
1/6/2025
cs.CLcs.AI

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Abstract

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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Cite this paper

@article{wehnert2025analyzing,
  title  = {Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's Holistic Bias Dataset: Implications for Language Model Training},
  author = {Sabine Wehnert and Muhammet Ertas and Ernesto William De Luca},
  year   = {2025},
  eprint = {2501.03324},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.03324v1}
}

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