Legal Judgment Prediction
Legal judgment prediction is a machine learning approach that forecasts court decisions and judicial outcomes based on case features, legal precedent, and judicial characteristics. Pioneered by Daniel Katz and colleagues in 2017 with their celebrated U.S. Supreme Court prediction model, this method applies supervised learning to large datasets of digitized court decisions to identify patterns in how judges decide cases. Legal judgment prediction has since expanded to appellate courts, trial courts, and international tribunals, enabling legal professionals to anticipate case outcomes and make strategic litigation decisions.
Dossier source
Citations copiées telles quelles du dossier source de la méthode. Aucune vérification au niveau de la revendication n'en est déduite.
- Katz, D. M., Bommarito, M. J., & Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PLOS One, 12(4), e0174698. · DOI 10.1371/journal.pone.0174698
- Matz, D., & Spicer, J. (2019). Predicting judicial decisions of the European Court of Human Rights. Artificial Intelligence and Law, 27(2), 123-145. · URL
- Lage-Freitas, A., de Oliveira Santini, F., Praxedes Filho, P. H., & de Almeida Oliveira, A. (2022). Predicting Supreme Federal Court decisions by explainable machine learning. Frontiers in Artificial Intelligence, 4, 586561. · URL
Revendications organisées
Revendications enregistrées dans le registre de preuves, chacune avec sa propre évaluation.
Cette vue n'invente pas d'évaluation de revendication lorsque le registre n'en contient aucune.
Méthodes apparentées
Généré à partir du graphe de méthodes et présenté comme des relations suggérées par la machine — aucune revendication de preuve n'est déduite.