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

TL;DR

This paper investigates biases in Swiss legal judgment data using Facebook's Holistic Bias dataset and NLP techniques to understand their impact on language models, aiming to promote fairer legal AI applications.

Contribution

It introduces a novel analysis of bias in legal NLP datasets using holistic bias descriptors and advanced visualization methods, highlighting challenges and implications for fair AI.

Findings

Biases influence model predictions in legal NLP tasks

Dataset imbalance affects model performance

Attention visualization reveals bias impact on decision-making

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.