Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Khatun, Sofia; K., Sivananda Kumar Title A name given to the resource Assessing the Efficacy of Artificial Intelligence (AI) Applications in Predictive Policing: A Systematic Review Method Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Digital Strategy and Governance in Transformative Technologies;pp.94-118 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003477808-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003477808-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016775690?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016775690?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Khatun S.; K. S.K., School of Law, Christ University, Bangalore, India Description An account of the resource Artificial intelligence (AI) has gained attention for its potential to improve law enforcement operations through proactive policing. Advancements in data science have shown the potential benefits of applying machine learning (ML) in the criminal justice sector. Therefore, research in improving methods to forecast the likelihood of criminal reoffending is quickly growing. Creating a cutting-edge model for using ML to predict recidivism is challenging. We picked 12 out of 79 studies from Scopus and PubMed online databases in a comprehensive review that ensures the models can be replicated across various datasets and are suitable for predicting recidivism. Using two specific measures, the 12 research compared different datasets and machine learning algorithms. This study demonstrates that each approach achieves strong performance, with an average accuracy score of 0.81 and an average area-under-the-curve score of 0.74. This systematic research emphasizes essential factors that could enable criminal justice professionals to consistently utilize forecasts of recidivism risk generated by machine learning approaches. The factors include performance indicators, transparent algorithms or explainable AI approaches, and high-quality input data. 2026 Sofia Khatun, K. Sivananda Kumar. All rights reserved. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104040914-5; 978-103276271-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online