Abstract
Aim: The paper examines the role of artificial intelligence (AI) in law enforcement training and highlights the benefits and challenges associated with its use. AI in training can provide law enforcement officials with real-time, customised feedback on their performance, which can help them improve their skills and knowledge. However, the use of AI in law enforcement training also raises several challenges, such as ethical concerns related to the use of personal data and potential biases in AI algorithms. Therefore, it is crucial to effectively prepare law enforcement personnel to utilise AI to its full potential while minimising potential harm.
Methodology: The paper also explores the new digital strategy of the European Union Agency for Law Enforcement Training (CEPOL) and how it incorporates AI technology into its training programmes to equip law enforcement authorities with the most up-to-date knowledge and skills. Additionally, the paper underscores the importance of research and science in identifying and developing new AI advancements and best practices.
Findings: Finally, this article presents the identified gaps in digital skills and the use of new technologies in law enforcement training.
Value: CEPOL periodically collects data to define strategic training priorities for law enforcement officials, emphasising digital skills and the use of new technologies. The need to increase the knowledge of law enforcement representatives on the rules of responsibility for artificial intelligence in the field of internal security remains a constant priority accompanying the development of technologies used in the work of law enforcement authorities.
References
Angelov, P. & Gu, X. (2018). Towards Anthropomorphic Machine Learning. The Expanding Frontier of Artificial Intelligence. Computer, 51(9), 28-36. https://doi.org/10.1109/MC.2018.3620973
Celik I. et al. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: A Systematic Review of Research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528- 022-00715-y
Chubb, J., Cowling, P. & Reed, D. (2022). Speeding up to keep up: exploring the use of AI in the research process. AI & Soc 37, 1439–1457. https://doi.org/10.1007/s00146-021-01259-0
Felzmann, H., Villaronga, E. F. & Lutz, C. et al. (2020). Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data & Society, 6(1), 1-14. https://doi.org/10.1177/2053951719860542
Gesk, T. S. & Leyer, M. (2022). Artificial intelligence in public services: When and why citizens accept its usage. Government Information Quarterly, 39(3), 101704. https://doi.org/10.1016/j.giq.2022.101704
Hayward, K. J. & Maas, M. M. (2020). Artificial intelligence and crime. A primer for criminologists. Crime Media Culture, 17(2), 209-233. https//www.doi:10.1177/1741659020917434
Lotfi, I. & El Bouhadi, A. (2022). Artificial Intelligence Methods: Toward a New Decision-Making Tool. Applied Artificial Intelligence, 36(1), e1992141. https://doi.org/10.1080/08839514.2021.1992141
Lunhol, O. & Torhalo, P. (2024). Artificial Intelligence in Law Enforcement: current state and development prospects. Proceedings of Socratic Lectures, 10, 120-124. https://doi.org/10.55295/PSL.2024.II12
Mager, R. F. & Pipe, P. (1979). Analyzing performance problems. Lake Publishing. https://hptmanualaaly.weebly.com/mager-and-pipes-model.html
McGehee, W., & Thayer, P. (1961). Training in business and industry. Wiley.
Montasari, R. (2022). Artificial Intelligence and National Security. Springer.
Ntoutsi, E., Fafalios, P. & Gadiraju U. et al. (2020). Bias in data-driven artificial intelligence systems - An introductory survey. WIREs Data Mining Knowl Discov, 10(6), 1356. https://doi.org/10.1002/widm.1356
Poushter, J. et al. (2016). Smartphone ownership and internet usage continues to climb emerging economies. Pew Research Center, 22, 1-44.
Rademacher, T. (2020). Artificial Intelligence and Law Enforcement. In T. Wischmeyer, & T. Rademacher (Eds.), Regulating Artificial Intelligence (pp. 225-254). Springer. https://doi.org/10.1007/978-3- 030-32361-5
Trutkowski, C. (2016). Training needs analysis and national training strategies Toolkit. Centre of Expertise for Local Government Reform. Council of Europe.
Vidu, C., Zbuchea, A., Mocanu, R. & Pinzaru, F. (2020). Artificial Intelligence and the Ethical Use of Knowledge. Strategica.
Zhu L. et al., (2021). Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter-based resources. High Volt, 6(6), 924-937. https://doi.org/10.1049/hve2.12157
Zhu, H. & Jin, Y. (2019). Multi-objective Evolutionary Federated Learning. In IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1310-1322.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2025 Academic Journal of Internal Affairs
