Explainable machine learning multi-label classification of Spanish legal judgements

TL;DR

This paper introduces a hybrid AI system that classifies Spanish legal judgments into multiple labels and provides natural language explanations, enhancing transparency and aiding legal practitioners in understanding case decisions.

Contribution

The work presents the first multi-label legal judgment classification system with integrated natural language explanations, combining machine learning, NLP, and legal reasoning.

Findings

Achieved over 85% micro precision on legal judgment classification

Provided natural language explanations with comparable quality to human experts

Demonstrated usefulness in reducing manual legal classification workload

Abstract

Artificial Intelligence techniques such as Machine Learning (ML) have not been exploited to their maximum potential in the legal domain. This has been partially due to the insufficient explanations they provided about their decisions. Automatic expert systems with explanatory capabilities can be specially useful when legal practitioners search jurisprudence to gather contextual knowledge for their cases. Therefore, we propose a hybrid system that applies ML for multi-label classification of judgements (sentences) and visual and natural language descriptions for explanation purposes, boosted by Natural Language Processing techniques and deep legal reasoning to identify the entities, such as the parties, involved. We are not aware of any prior work on automatic multi-label classification of legal judgements also providing natural language explanations to the end-users with comparable overall quality. Our solution achieves over 85 % micro precision on a labelled data set annotated by legal experts. This endorses its interest to relieve human experts from monotonous labour-intensive legal classification tasks.