{"title":"Artificial intelligence in police research: a preliminary examination of feasibility and replication","authors":"Kyle McLean, Jeffrey Rojek, Justin Nix","doi":"10.1007/s11292-026-09754-4","DOIUrl":null,"url":null,"abstract":"Objectives This manuscript tests whether artificial intelligence (AI) review of body-worn camera footage replicates the findings of a randomized-controlled trial of police de-escalation training using systematic social observation (SSO). Methods Body-worn camera video that was previously analyzed using SSO was subjected to review by an AI program. The study then replicates the analysis of the randomized-controlled trial using outcomes generated by AI. The results are compared to those of the original study. Results All five measures produced by the AI correlated with at least one SSO measure, though several SSO measures appeared completely unrelated to any measure produced by AI. The analysis of treatment effects by AI reaches the same conclusion as the original study. Conclusions SSO provides greater nuance to understanding police interactions but is weakened by its time-consuming and expensive nature. AI provides a promising avenue for conducting similar analyses more quickly and efficiently.","PeriodicalId":47684,"journal":{"name":"Journal of Experimental Criminology","volume":"14 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2026-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental Criminology","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1007/s11292-026-09754-4","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRIMINOLOGY & PENOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives This manuscript tests whether artificial intelligence (AI) review of body-worn camera footage replicates the findings of a randomized-controlled trial of police de-escalation training using systematic social observation (SSO). Methods Body-worn camera video that was previously analyzed using SSO was subjected to review by an AI program. The study then replicates the analysis of the randomized-controlled trial using outcomes generated by AI. The results are compared to those of the original study. Results All five measures produced by the AI correlated with at least one SSO measure, though several SSO measures appeared completely unrelated to any measure produced by AI. The analysis of treatment effects by AI reaches the same conclusion as the original study. Conclusions SSO provides greater nuance to understanding police interactions but is weakened by its time-consuming and expensive nature. AI provides a promising avenue for conducting similar analyses more quickly and efficiently.
期刊介绍:
The Journal of Experimental Criminology focuses on high quality experimental and quasi-experimental research in the advancement of criminological theory and/or the development of evidence based crime and justice policy. The journal is also committed to the advancement of the science of systematic reviews and experimental methods in criminology and criminal justice. The journal seeks empirical papers on experimental and quasi-experimental studies, systematic reviews on substantive criminological and criminal justice issues, and methodological papers on experimentation and systematic review. The journal encourages submissions from scholars in the broad array of scientific disciplines that are concerned with criminology as well as crime and justice problems.