The Impact of Citizens’ Perception of Police Legitimacy on the Willingness to Accept AI CCTV Technology

AI technology is increasingly being explored as a strategic tool for enhancing security. However, concerns surrounding privacy infringement and inherent biases in such technologies have raised significant challenges to their widespread acceptance. Real-time facial recognition and object recognition systems, in particular, necessitate public empathy and support to function effectively. Police legitimacy and procedural justice have emerged as critical mechanisms for fostering public trust and cooperation in law enforcement activities. Understanding the role of police legitimacy in shaping public acceptance of AI-driven strategies like AI CCTV is crucial in the context of public security. This study, grounded in Tyler's procedural justice theory, sought to examine the influence of perceptions of police legitimacy on citizens' willingness to accept AI CCTV and their readiness to share personal information. A survey of 1,101 general citizens was conducted to explore these dynamics. The findings confirmed procedural fairness as a key factor, as argued by Tyler. Procedural fairness positively influenced the acceptance of AI CCTV through its indirect effects on compliance obligations, trust in the police, and a willingness to provide personal information. Furthermore, police effectiveness was shown to have a positive indirect impact on AI CCTV acceptance, mediated through trust in the police and willingness to share information. In contrast, distributive fairness did not significantly affect citizen cooperation and was found to negatively influence the acceptance of AI technology. This negative association may reflect a tendency to view technological solutions as alternatives to address perceived shortcomings in police performance. These findings underscore the importance of procedural justice in shaping public attitudes toward AI-based security measures. The research offers meaningful insights for law enforcement agencies in formulating effective security strategies that incorporate AI CCTV. It also provides a foundation for developing targeted approaches to achieve public consensus and support. Policymakers can utilize these results as evidence to guide the creation of security policies that balance technological innovation with public trust, ensuring the legitimacy and fairness of AI-driven solutions in public security contexts.