Computational analysis demonstrates automated classification of court decisions using a taxonomy-based AI framework, suggesting new pathways for evidence-based judicial reform.
This presentation explores the use of artificial intelligence and data-driven methods to analyse large-scale collections of court decisions and identify patterns in judicial proceedings. The authors present a taxonomy-based approach to the automated classification of procedural court documents, demonstrating how structured judicial data can support deeper analysis of case proceedings and contribute to more evidence-based approaches to judicial research and reform. The presentation was delivered by Iryna Izarova, Yurii Prytyka and Yuliia Hartman from Taras Shevchenko National University of Kyiv at the Legal Case Analytics & Modeling (LCAM): Mathematics meets Law event, organised by Linnaeus University and Taras Shevchenko National University of Kyiv on 18–21 August 2026 within the LCAM: Legal Case Law Analytics & Modeling (2024–2026) project, funded by the Swedish Institute (SI).
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Izarova et al. (2026) studied this question.
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