Artificial Intelligence Adoption in Predictive Policing to Predict Crime Mitigation Performance

Abstract

The global adoption of AI-powered predictive policing, utilizing big data, is becoming a prevalent strategy for crime control and law enforcement enhancement. Recognizing its potential, Abu Dhabi Police places emphasis on officer training and collaborative efforts for crime prevention. As the integration of predictive policing continues within Abu Dhabi Police, the importance of exploring the value of training and collaborative learning becomes even more crucial (Abu Dhabi Police GHQ, 2020). This study's objective is to uncover the intricate relationship between crime mitigation performance and key factors, encompassing Predictive Policing Adoption, Specialised Technology Training, Innovative Officer Performance, and Collaborative Learning. Questionnaire survey was used to collect data from participants who are employees of the Abu Dhabi Crime Scene Department. A total of 316 valid responses were used in the development of multi-linear regression model to predict crime mitigation performance. By utilizing the developed multi-linear regression model, stakeholders can forecast Crime Mitigation Performance (CMP) by substituting the values of Predictive Policing Adoption (PPA), Specialised Technology Training (STT), Innovative Officer Performance (IOP), and Collaborative Learning (CL) into the formula. This predictive tool offers the Abu Dhabi Crime Scene Department a valuable resource to proactively assess and plan for crime mitigation outcomes, enhancing their strategic decision-making capabilities and fostering a more effective approach to law enforcement operations