Analysis of Factors Influencing the Adoption of Artificial Intelligence for Crime Management
Despite the benefits of Artificial Intelligence (AI) and its potential to produce deep insights and predictions, its adoption and usage are still limited in the area of crime management. Over the years, crime rates have been increasing in India, and law e
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Abstract. Despite the benefits of Artificial Intelligence (AI) and its potential to produce deep insights and predictions, its adoption and usage are still limited in the area of crime management. Over the years, crime rates have been increasing in India, and law enforcement agencies face enormous challenges given the increasing population, urbanization, limited resources, and ineffective conventional models of reactive and investigative policing. There is an unprecedented opportunity for AI to be leveraged together with new policing models such as intelligence-led policing and predictive policing for effective crime management. In this research-in-progress paper, we offer a deeper understanding of factors significant for the adoption intention of AI for crime management in India. Further, on the practical front, the study will help law enforcement agencies to effectively leverage AI and implement innovative policing models for crime management. Keywords: Artificial Intelligence
Crime management AI adoption
1 Introduction Law enforcement agencies (LEA) in India are facing tremendous challenges in crime management due to several factors such as increasing crime rates, low police per person ratio, increasing population, and ineffective models of policing. The nature and methods of committing crimes are also changing rapidly, posing serious challenges to law enforcement agencies and other stakeholders of the criminal justice system in India. The models of reactive, investigative policing are not enough to contain the increase in crimes. There is a compelling opportunity for implementing new policing models such as Intelligence-led policing (ILP) and Predictive policing to be more effective with limited resources. For example, there are vast volumes of data that are being collected internally that could be used for crime analysis and to influence police decision making. Predictive policing aims to rely on computer algorithms to see patterns, predict the occurrence of future events based on large quantities of data and aims © IFIP International Federation for Information Processing 2020 Published by Springer Nature Switzerland AG 2020 S. K. Sharma et al. (Eds.): TDIT 2020, IFIP AICT 617, pp. 3–9, 2020. https://doi.org/10.1007/978-3-030-64849-7_1
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to carefully target police presence to the necessary minimum to achieve desired results (Sanders and Sheptycki 2017). The main objective of Intelligence-led policing is to apply crime data analysis to objectively inform policy, policing strategies, and tactical operations to reduce and prevent crime emphasizing the proactive use of police resources (Ratcliffe 2016). However, such approaches are not easy to implement using manual processes and existing infrastructure. There is growing interest in using digital technologies to address the challenges faced by law enforcement agencies in India. The use of data to detect crimes has long been a central feature in the security policies’ decision-making process (Peeters and Schuilenburg 2018). The fast
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