The Use of Artificial Intelligence Technology in Preventing Female Delinquency: A Novel Approach to Situational Crime Prevention

Abstract

In recent years, artificial intelligence (AI) has garnered significant attention as an effective tool across various domains of criminal sciences, particularly in crime prevention. This article adopts a descriptive-analytical approach to examine the role of AI in the prevention of female delinquency. Given the distinct characteristics of the female offender population and the social, economic, and psychological factors influencing their criminal behavior, the present study seeks to explore the potential of modern technologies—particularly machine learning, big data analytics, and predictive algorithms—in the early identification of high-risk behaviors, crime prediction, and timely intervention. Situational prevention of female delinquency through the application of AI technology represents an innovative and targeted approach in criminal policy-making. By focusing on three core areas, it demonstrates considerable effectiveness in reducing delinquent behaviors. First, by increasing the effort and risk associated with committing crimes—through the use of predictive crime algorithms, intelligent offender profiling systems, and recidivism pattern analysis—it becomes more difficult for women to engage in criminal activities. Second, the reduction of the benefits associated with delinquency is achieved through tools such as geographic analysis, crime mapping, and surveillance robots, which restrict immediate access to criminogenic settings. Finally, the elimination of excuses for criminal behavior is addressed through regulatory reforms, precise rule-setting, and the facilitation of norm enforcement via intelligent systems and machine learning processes.