Legal challenges of regulating artificial intelligence in law enforcement, taking into account the interdisciplinary approach to socio-legal transformations

This study examined the legal challenges associated with regulating artificial intelligence in law enforcement, considering its broader socio-legal transformations. Using an interdisciplinary approach, the research conducted a comparative legal analysis of AI regulations across multiple jurisdictions, assessing statutory frameworks, judicial decisions, and policy initiatives. The study identified key regulatory gaps, including the absence of clear statutory definitions, issues of accountability in AI-driven policing, and the risk of algorithmic bias. Through a comparative analysis of different jurisdictions, the study highlighted significant disparities in AI governance. The European Union adopts a precautionary and rights-based regulatory approach, classifying law enforcement AI as high-risk and imposing strict compliance requirements. The United States relies on decentralised governance, where state-level initiatives and judicial oversight shape AI deployment, leading to inconsistent enforcement. China prioritises state control, rapidly integrating AI surveillance into law enforcement without independent oversight, raising concerns about due process and human rights protections. In contrast, emerging jurisdictions like Kazakhstan and Kyrgyzstan are in the early stages of AI regulation, implementing AI-driven policing technologies while relying on general legal provisions. The study found that AI’s integration into law enforcement presents both opportunities and risks. While AI enhances policing efficiency and resource allocation, it also reinforces systemic biases, compromises data privacy, and challenges traditional accountability structures. The absence of regulatory harmonisation further complicates cross-border AI cooperation and liability determination. These findings underscored the need for clearer legal frameworks, international coordination, and ethical AI governance to balance security imperatives with fundamental rights. Given the rapid pace of AI adoption, future research should focus on empirical assessments of AI’s real-world impact on legal systems, public trust, and law enforcement transparency, ensuring that regulatory approaches remain adaptive and equitable