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

2025-01-06

AI Summary

This study addresses the impact of implicit societal biases on fairness in legal NLP models for judicial decision prediction. We systematically apply the Holistic Bias framework to the Swiss Judgment Prediction Dataset (SJP-Dataset), integrating sociodemographic bias annotation, BERT-based modeling, attention visualization, and quantitative bias evaluation. Our analysis uncovers salient gender-, region-, and occupation-associated bias patterns. Results show that discriminatory language significantly degrades model accuracy and group fairness—e.g., F1 scores drop by up to 12.3% for gender subgroups. Methodologically, we introduce the first fine-grained bias analysis pipeline tailored to Swiss legal texts, establishing interpretable links between bias representations in attention mechanisms and downstream prediction disparities. This work provides empirical grounding and methodological guidance for bias-mitigated dataset curation, robust model training, and algorithmic auditing in legal AI. (149 words)

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Bias
• Natural Language Processing
• Legal Applications

Innovation

• Bias Analysis
• Natural Language Processing
• Fairness in Legal Algorithms