JuDDGES
Judicial Decision Data Gathering, Encoding and Sharing — an Open Science platform for scalable, transparent, human-validated judicial decision analysis across jurisdictions.
Powered by Margin-Aware Active Learning
The system can prioritise the most informative decisions for expert review (e.g., ambiguous/low-confidence cases), improving consistency and reducing annotation cost over time.Project Overview
JuDDGES harnesses state-of-the-art Natural Language Processing and Human-in-the-Loop (HITL) methods to enable scalable, flexible, and on-going meta-annotation — combining automated suggestions with domain experts in-the-loop.
The platform supports legal records and judgments across jurisdictions, including England & Wales, Poland, and partner work in Lyon (France).
Why this matters
We reduce barriers of resources, language, and data/format inhomogeneity that impede research on judicial decision-making. Open tools + open outputs enable researchers to empirically test theories and inform evidence-led policy questions.
How the platform works (AI + Human-in-the-Loop)
Workflow
- Ingest legal decisions into a searchable corpus
- Suggest topics/codes using NLP models
- Validate with expert review (accept / reject / correct)
- Audit decisions and maintain traceability
- Export structured datasets for analysis (CSV/SPSS-ready)
What you can do
Open resources
Zenodo Community
Open outputs: coding schemes, reports, workshop materials, and publications.
Datasets (HuggingFace)
Project datasets and structured releases: https://huggingface.co/JuDDGES
Selected publications
Abbas, W., Zia, T., Tirunagari, S., Chennareddy, V., Dhami, M., & Windridge, D. (2026). The Application of Pre-trained Transformer Models to UK Court of Appeal Legal Judgments. Springer Nature. DOI
Dhami, M., Kajdanowicz, T., Windridge, D., & Boukacem-Zeghmouri, C. (2025). Judges on Trial: The Future of Research on Judicial Decision-Making. Wiley. DOI
Tirunagari, S., Dhami, M., & Windridge, D. (2025). Margin-aware Active Learning for User-adaptive Text Classification. PDF
Funding, Acknowledgements & Contact
Project funded by:
Engineering and Physical Sciences Research Council (EPSRC)
Grant EP/Y035992/1 – Judicial Decision Data Gathering, Encoding and Sharing
Supported under the CHIST-ERA Call ORD “Open & Re-usable Research Data & Software”
Contact:
Prof. Mandeep K. Dhami
Prof. David Windridge
We also thank Mahensingh Deonaran (Middlesex University) for curating the initial dataset version and supporting early data preparation.