Strategy

Strategy

The Role of Artificial Intelligence in Transforming Police Science: A Case Study of Modern Technological Applications

Document Type : Research Paper

Authors
Mohammad Rajabi 1
Hadi Rajabi 2
1 Assistant Professor of Political Science, Department of Security and Social Sciences, Faculty of Security and Policing and Social Sciences, Policing Sciences and Social Studies Research institute, Tehran, Iran.
2 Faculty Member, Amin University of Police Sciences, Tehran, Iran
10.22034/rahbord.2025.511433.1792
Abstract
Recent technological advancements have profoundly transformed various fields, including police science. Among these developments, artificial intelligence (AI) has become a key driver in enhancing crime prevention, behavioral analysis, surveillance, and crisis management. Its effective application, however, requires more than mere access to technology; it depends on coherent policymaking, the development of adequate infrastructure, and the training of specialized human resources. This study explores how AI is reshaping the field of police science and identifies key strategies for its effective and sustainable implementation. Drawing on qualitative, library-based, and documentary methods, the research reviews the core concepts of AI and police science and investigates the potential benefits and limitations of AI adoption in policing. The findings suggest that AI offers substantial advantages in predictive policing, big data analytics, transparency, efficient resource allocation, and the reduction of human error. At the same time, significant challenges such as privacy concerns, algorithmic bias, high implementation costs, legal complexities, and cybersecurity risks must be addressed. The study concludes that maximizing the benefits of AI in policing demands not only technical and legal readiness but also strategic foresight. A deliberate focus on building domestic capacities and reducing dependence on foreign technologies is essential to ensure both ethical standards and technological sovereignty in future law enforcement practices.
 
Keywords
Modern technologies
artificial intelligence
transformation
police science
big data

Subjects

  • Abouzari, M. (2022). The impact of artificial intelligence on the quality of criminal investigations. Quarterly Journal of Law and New Technologies, 3(6), 1–13. [in Persian]
  • Afshar, M. M., Barzegar, K., & Kiani, D. (2020). Identification of cyber space megatrends affecting the future of public diplomacy: A structural and cross-impact analysis approach. Strategic Studies Quarterly, 29(1), 97–129. [in Persian]
  • Agha Pirouz, A. (2021, May 26). A reflection on the nature and mechanism of transformation: Transformation is impossible without management. Retrieved from the official website of the Office for the Preservation and Publication of the Works of Grand Ayatollah Khamenei: https://khl.ink/f/47902 [in Persian]
  • Ahmadi Baladehi, S. M. (2024). Investigating the application of artificial intelligence in the prevention and detection of crimes by repeat offenders in Mazandaran Province. Artificial Intelligence Quarterly, 1(1), 18–24. [in Persian]
  • Alikhademi, K., Drobina, E., Prioleau, D., Richardson, B., & Purves, D. (2021). A review of predictive policing from the perspective of fairness. Artificial Intelligence and Law, 29(2), 133–155.
  • Alikhademi, K., Drobina, E., Prioleau, D., Richardson, B., & Purves, D. (2021). A review of predictive policing from the perspective of fairness. Artificial Intelligence and Law, 29(2), 133–155.
  • Avorzamani, H., & Safaei Nabat, H. (2016). The role of data mining and artificial intelligence in preventing violent crimes. Crime Prevention Studies Quarterly, 11(38), 146–170. [in Persian]
  • Azmand, F. (2024). Investigating the impact of artificial intelligence on urban security. Master’s thesis, Department of Geography, Faculty of Literature and Humanities, Ferdowsi University of Mashhad. [in Persian]
  • Bayley, D. H. (1994). Police for the future. Oxford University Press.
  • Bayley, D. H. (1994). Police for the future. Oxford University Press.
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679.
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Brown, T. (2020). Artificial Intelligence and Predictive Policing: Opportunities and Challenges. Policing Technology Review, 22(2), 40–55.
  • Brown, T. (2020). Artificial intelligence and predictive policing: Opportunities and challenges. Policing Technology Review, 22(2), 40–55.
  • Burns, J. M. (1978). Leadership. Harper & Row.
  • Burns, J. M. (1978). Leadership. Harper & Row.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
  • Dovali, M. M., & Nohehgar, S. (2023). The impact of artificial intelligence on employee performance and job engagement: The moderating role of transformational leadership. Police Organizational Development Quarterly, 88(21), 11–46. [in Persian]
  • Ferguson, A. G. (2017). The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. NYU Press.
  • Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. NYU Press.
  • Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8-12.
  • Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8–12.
  • Hosseini-Moghadam, M. (2022). Artificial intelligence and the future-oriented strategic outlook of universities in Iran: An analysis of goals and stakeholders. Strategic Studies Quarterly, 31(3), 441–466. https://doi.org/10.22034/rahbord.2023.382428.1533 [in Persian]
  • Human Rights Commission. (2019). Privacy and Ethics in AI for Crisis Management. Ethics in AI Journal, 10(4), 7-12.
  • Human Rights Commission. (2019). Privacy and ethics in AI for crisis management. Ethics in AI Journal, 10(4), 7–12.
  • Jahankhani, H., Akhgar, B., Cochrane, P., & Dastbaz, M. (Eds.). (2020). Policing in the Era of AI and Smart Societies. Springer.
  • Jahankhani, H., Akhgar, B., Cochrane, P., & Dastbaz, M. (Eds.). (2020). Policing in the era of AI and smart societies. Springer.
  • Kaur, M., & Saini, M. (2024). Role of Artificial Intelligence in the crime prediction and pattern analysis studies published over the last decade: a scientometric analysis. Artificial Intelligence Review, 57, Article number 202.
  • Kaur, M., & Saini, M. (2024). Role of artificial intelligence in the crime prediction and pattern analysis studies published over the last decade: A scientometric analysis. Artificial Intelligence Review, 57, Article 202.
  • Khamenei, S. A. (2024, August 27). Remarks in the first meeting with the 14th cabinet. Retrieved from the official website of the Office for the Preservation and Publication of the Works of Grand Ayatollah Khamenei: https://khl.ink/f/57426 [in Persian]
  • Malek-Mohammad, M. B., Sazdar, A. M., & Aslinajad, M. (2023). Investigating the impact of artificial intelligence capabilities on university staff training. ICT in Law Enforcement Scientific Journal, 4(16), 10–18. [in Persian]
  • Meijer, A., & Wessels, M. (2019). Predictive Policing: Review of Benefits and Drawbacks. International Journal of Public Administration, 42(12), 1031-1039.
  • Meijer, A., & Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031–1039.
  • Miller, K., & Thompson, D. (2018). AI and Law Enforcement: A New Era of Predictive Analytics. Crime Prevention Studies, 15(4), 28–35.
  • Miller, K., & Thompson, D. (2018). AI and law enforcement: A new era of predictive analytics. Crime Prevention Studies, 15(4), 28–35.
  • Mohammadi-Nia, O., & Alinajad, A. (2023). The role and impact of artificial intelligence in the process of crime detection. Kargah Scientific Journal, 17(63), 85–104. [in Persian]
  • National Institute of Justice. (2020). Predictive Policing and Artificial Intelligence: Tools for Modern Law Enforcement. Washington, D.C.: NIJ Press.
  • National Institute of Justice. (2020). Predictive policing and artificial intelligence: Tools for modern law enforcement. Washington, D.C.: NIJ Press.
  • Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. RAND Corporation.
  • Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. RAND Corporation.
  • Pourmohammad, M. (2023). The use of artificial intelligence in preventing crimes by repeat offenders. The 2nd International Conference on Research in Accounting, Management, Economics, and Humanities. Retrieved from: https://civilica.com/doc/1774354 [in Persian]
  • Raeisi Vanani, R., & Vafadar, R. (2015). An analysis of the position of futures studies in the production of police knowledge. Police Knowledge Research Quarterly, 17, 17–40. [in Persian]
  • Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.
  • Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • Sachoulidou, A. (2023). Going beyond the 'common suspects': to be presumed innocent in the era of algorithms, big data and artificial intelligence. Artificial Intelligence and Law.
  • Sachoulidou, A. (2023). Going beyond the 'common suspects': To be presumed innocent in the era of algorithms, big data and artificial intelligence. Artificial Intelligence and Law.
  • Smith, R., & Jones, M. (2021). Social Media Analytics for Crisis Management Using AI. Journal of Computer Science and Cybersecurity, 18(1), 100-110.
  • Smith, R., & Jones, M. (2021). Social media analytics for crisis management using AI. Journal of Computer Science and Cybersecurity, 18(1), 100–110.
  • Wu, H., Wang, Z., & Li, X. (2021). Applications of Artificial Intelligence in Disaster Management: Challenges and Opportunities. Journal of Disaster Science, 15(2), 20-30.
  • Wu, H., Wang, Z., & Li, X. (2021). Applications of artificial intelligence in disaster management: Challenges and opportunities. Journal of Disaster Science, 15(2), 20–30.
  • Zhang, T., Liu, H., & Chen, Y. (2020). Real-Time Disaster Response with AI: A Comprehensive Review. Natural Hazards Review, 21(3), 50-65.
  • Zhang, T., Liu, H., & Chen, Y. (2020). Real-time disaster response with AI: A comprehensive review. Natural Hazards Review, 21(3), 50–65.

  • Receive Date 08 January 2025
  • Revise Date 12 February 2025
  • Accept Date 16 March 2025