Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's Holistic Bias Dataset: Implications for Language Model Training
It addresses bias in legal NLP for fair decision-making, but is incremental as it applies existing methods to a new dataset.
This study analyzed biases in the Swiss Judgment Prediction Dataset using Facebook's Holistic Bias dataset and NLP techniques like attention visualization, identifying biases and their impact on model predictions, though challenges like dataset imbalance and token limits affected performance.
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.