Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
This work addresses the access-to-justice gap for legal professionals and the public by automating the processing of court decisions, though it is incremental as it applies existing NLP techniques to a new legal dataset.
The paper tackled the problem of information asymmetry in the legal system by developing NLP methods to extract entities from French appeal court decisions, constructing networks of lawyers and judgments, and proposing metrics to rank lawyers and assess case difficulty, resulting in tools like lawyer rankings based on experience and win/loss ratios.
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.