Overloaded legal research
Legal teams spend too much time reviewing fragmented rulings and matching them to new cases.

Judicial Intelligence Platform
The project brings new accessibility and analysis of judicial decisions across varied legal systems using advanced Natural Language Processing and Human-In-The-Loop technologies.
Duration
To be confirmed
Status
Active
Focus
Consistent legal reasoning and faster preparation for complex cases.
Legal teams spend too much time reviewing fragmented rulings and matching them to new cases.
Similar situations can produce different outcomes, making legal reasoning harder to standardize.
Preparing clear case summaries and argument drafts manually delays decision support.
Capture facts, context, and constraints in a structured way before legal analysis starts.
Use semantic retrieval to find similar judicial decisions and relevant supporting material.
Surface common and conflicting argument patterns across retrieved rulings.
Generate concise, editable summaries that accelerate expert legal review.
The JuDDGES project aims to revolutionize the accessibility and analysis of judicial decisions across varied legal systems using advanced Natural Language Processing and Human-In-The-Loop technologies. It focuses on criminal court records from jurisdictions with diverse legal constitutions, including Poland and England & Wales. By overcoming barriers related to resources, language, data, and format inhomogeneity, the project facilitates the development and testing of theories on judicial decision-making and informs judicial policy and practice. Open software and tools produced by the project will enable extensive, flexible meta-annotation of legal texts, benefiting researchers and public legal institutions alike. This initiative not only advances empirical legal research by adopting Open Science principles but also creates the most comprehensive legal research repository in Europe, fostering cross-disciplinary and cross-jurisdictional collaboration.
Duration: 15.01.2024 - 14.01.2026
Funding: 529 384,67 EUR