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	Predykcja wyroków sądowych	Analiza sieciowa orzecznictwa
Dziedzina	Nauki sądowe	Nauki sądowe
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2017	2011
Twórca	Daniel Katz	James Fowler
Typ	Computational law and judicial decision prediction method	Network science and legal informatics method
Źródło pierwotne	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.
Inne nazwy	court outcome prediction, judicial decision prediction, legal AI forecasting	citation network analysis, legal precedent mapping, case law graph analysis
Pokrewne	3	3
Podsumowanie	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.