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	Predykcja wyroków sądowych	Analiza powiązań przestępstw
Dziedzina	Nauki sądowe	Nauki sądowe
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2017	2002
Twórca	Daniel Katz	Craig Bennell
Typ	Computational law and judicial decision prediction method	Crime science and offender profiling 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.	Bennell, C., Canter, D. V., & Alison, L. J. (2002). Linking commercial burglaries by modus operandi: Tests using regression and ROC analysis. Science and Justice, 42(3), 153-164.
Inne nazwy	court outcome prediction, judicial decision prediction, legal AI forecasting	case linkage, offender linking, serial crime attribution
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.	Crime linkage analysis is a forensic method that determines whether a series of crimes were committed by the same offender based on behavioral and modus operandi (MO) similarities. Developed systematically by Craig Bennell and colleagues in the early 2000s, crime linkage applies statistical and similarity-matching techniques to establish offender attribution. The method is essential in serial crime investigation, where establishing linkage enables consolidation of investigation resources, geographic profiling, and offender-focused surveillance.