Book Title: Computational Criminology: AI Applications in Forensic Science and Criminal Justice
Editors: Dr. Xavier Louis, Dr. Surbhi Girdhar, Ms. Aswathi Chandran Nair, Mr. Ravi Kumar, and Ms. Nandini Katare
Chapter: 23
DOI: https://doi.org/10.59646/704/23
Author: Saumya Tripathi
Abstract
The application of machine learning to the prediction of legal case outcomes—whether a criminal defendant will be convicted, what sentence will be imposed, or how an appellate court will rule represents both a technically ambitious research programme and a set of practically deployed tools that are beginning to influence how litigants, prosecutors, defence attorneys, and judges approach case assessment and decision-making. This chapter examines the machine learning architectures employed in legal prediction systems, evaluates the empirical performance of models predicting conviction rates, sentencing outcomes, and appellate decisions, analyses the risks of strategic gaming and self-fulfilling prophecy that such systems introduce, and critically assesses the compatibility of AI-assisted legal decision-support with the ideals of individualised justice, adversarial fairness, and judicial impartiality. The chapter further addresses the specific challenge of natural language processing applied to legal text case summarisation, precedent retrieval, and contract review and evaluates emerging large language model applications to legal research and judicial analytics.