Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France

TL;DR

This paper presents an AI-based system that extracts legal data from French appeal court decisions, constructs networks of lawyers and judgments, and introduces metrics for ranking lawyers and assessing case difficulty.

Contribution

It introduces novel NLP techniques and network analysis methods for legal judgment data, enabling better understanding and visualization of legal proceedings.

Findings

Effective extraction of legal entities and data from judgments.

Network-based metrics for lawyer ranking and case difficulty assessment.

Community detection reveals case complexity patterns.

Abstract

Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of information of the legal system and the access-to-justice gap. We use NLP methods to extract interesting entities/data from judgments to construct networks of lawyers and judgments. We propose metrics to rank lawyers based on their experience, wins/loss ratio and their importance in the network of lawyers. We also perform community detection in the network of judgments and propose metrics to represent the difficulty of cases capitalising on communities features.