Comparar métodos

	Predicción de sentencias judiciales	Análisis de Redes de Jurisprudencia
Campo	Análisis forense	Análisis forense
Familia	Process / pipeline	Process / pipeline
Año de origen	2017	2011
Autor original	Daniel Katz	James Fowler
Tipo	Computational law and judicial decision prediction method	Network science and legal informatics method
Fuente seminal	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.	Lupo, G., & Bailey, J. (2014). Artificial intelligence and legal practice. Academic Press.
Alias	court outcome prediction, judicial decision prediction, legal AI forecasting	citation network analysis, legal precedent mapping, case law graph analysis
Relacionados	3	3
Resumen	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.	Network analysis of case law applies graph-theoretic and network science methods to study the structure and dynamics of legal precedent systems. Developed systematically by James Fowler and colleagues in 2011, this method treats legal citations as directed edges in a network where nodes represent court decisions and edges represent precedent relationships. By analyzing the topology of these networks, researchers uncover patterns in how law evolves, which precedents are most influential, and how legal doctrine spreads across jurisdictions.