Enhancing Criminological Theories with AI: A New Approach to Combat Human Trafficking

Abstract

Tthis research will endeavor to bring on board the use of AI in countering Human Trafficking utilizing the criminological theories to enhance the effectiveness of the methods being employed in the fight against this heinous crime. Hereby, applying the Routine Activity Theory (RAT), Social Learning Theory (SLT) and Strain Theory (ST) and the research is capable of illustrating directions where the AI can improve the architecture applied in the detection, prevention and intervention of the trafficking activities. Therefore, this paper is aimed at engaging the systematic literature review framework in order to evaluate the current literature and case studies in relation to AI in Human Trafficking. As clearly explained by authors in the indexes of the articles, the application of AI which includes; machine learning(ML), Natural Language Processing (NLP), and Social Network Analysis (SNA) enhance the strength of policing and decision making organizations. First of all, it can target parts of the problem, byl knowing where it is most likely to occur and can track it endlessly, both of which will improve resource management and early responding. However, it also states that the utility of AI have to be backed by ethical aspects which the paper identifies as privacy, bias, and accountability. Therefore, it should be noted that the integration of AI with the principles of the criminological theory can present ways of combating human trafficking, but such a process must be accompanied by the investigation of attainable problematic elements and the development of strategies to enforce the positive impacts of integrating AI while minimizing detrimental effects.