ARTIFICIAL INTELLIGENCE AND POLICING IN NIGERIA: IMPLICATIONS FOR SECURITY ADMINISTRATION AND SAFER COMMUNITIES
DOI:
https://doi.org/10.64415/jgpmc.v3i1.91Keywords:
Artificial Intelligence, Technology Acceptance Model, Policing, Nigeria Police Force, Security Administration, Safer CommunitiesAbstract
The increasing complexity of insecurity in Nigeria has heightened the need for innovative approaches that can strengthen security administration and improve public safety. Conventional policing approaches, which are largely reactive, continue to face challenges arising from limited intelligence capacity, inadequate resources, delayed responses, and increasingly sophisticated criminal networks. This paper examines the role of Artificial Intelligence (AI) in transforming policing and enhancing security administration in Nigeria, with emphasis on its contribution to crime prevention and safer communities. The paper anchored on the Technology Acceptance Model (TAM), to explain AI adoption through perceived usefulness, organizational readiness, and institutional capacity. Using a qualitative research approach, the paper draws on scholarly literature, policy documents, and secondary sources to explore the opportunities and challenges associated with AI-driven policing. The paper argues that AI can enhance intelligence-led policing, improve crime analysis and investigation, strengthen surveillance, and support evidence-based decision-making. However, its effective adoption is constrained by inadequate digital infrastructure, weak data systems, ethical concerns, privacy risks, limited technical expertise, and regulatory gaps. The paper concludes that AI should complement professional policing practices. It recommends that the Nigeria Police Force develop an AI adoption framework, while relevant government and security institutions invest in digital infrastructure, regulatory mechanisms, and capacity development to promote AI integration.
Keywords: Artificial Intelligence, Technology Acceptance Model, Policing, Nigeria Police Force, Security Administration, Safer Communities
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