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.
원본 기록
방법의 원본 기록에서 그대로 복사된 인용입니다. 이로부터 수준별 검증이 추론되지 않습니다.
- 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
큐레이션된 주장
각각 자체 평가와 함께 증거 원장에 유지된 주장입니다.
원장에 주장 평가가 없는 경우 이 보기에서는 주장 평가를 만들지 않습니다.
관련 방법
방법 그래프에서 생성되었으며 기계가 제안한 관계로 표시됩니다 — 증거 주장이 추론되지 않습니다.