Detection of Gender Bias in Legal Texts Using Classification and LLMs
Christian Javier Ratovicius, Jorge Andres Diaz-Pace, Antonela Tommasel · 2024
Gender bias is a common and often neglected issue in legal documents. It arises from perceptions or prejudices about the characteristics of a group, or the roles individuals should play in society. This bias can significantly impact the reasoning or outcomes of legal processes, such as judicial rulings. To ensure equal treatment for all individuals, it is crucial to effectively reduce this bias. The first step to reduce bias is to define approaches that can identify manifestations of gender bias. However, these manifestations are not usually easily detectable in text (e.g., through keywords) as they often require detailed contextual analysis, typically done manually by experts. This paper addresses this issue by leveraging natural language processing and machine learning techniques to automate parts of the analysis. Specifically, it proposes a processing pipeline based on text embeddings, binary classification, and the use of large language models (LLMs) to explain classification results. An initial evaluation on a set of judicial rulings shows promising results in terms of precision and recall, along with qualitative insights into the potential of these techniques in the legal domain.