Public attitudes towards police use of AI-driven face recognition technology

This study examined public attitudes toward police use of AI-driven facial recognition technology (FRT) for face detection, identification, verification, tracking, kinship verification, and masked perpetrator recognition. In a scenario-based survey with N = 507 participants, we investigated how perceptions of trust, fairness, accuracy, and support for specific FRT applications were influenced by general AI knowledge, trust in law enforcement, and application type. Masked face identification and kinship verification consistently received the lowest trust, fairness, accuracy, and support ratings, while face verification gathered the highest levels of acceptance. Contrary to expectations, deeper general AI knowledge was linked with decreased trust and support for FRTs in policing contexts. This suggests that technological literacy enhanced critical awareness of algorithmic limitations and ethical concerns. Participants expressed significant concerns about algorithmic bias, privacy implications, and surveillance capabilities. Trust in law enforcement emerged as the strongest predictor of FRT acceptance, indicating that acceptance of AI is embedded in broader socio-political relationships rather than determined by technological concerns alone. These findings contribute to our understanding of the social embeddedness of AI technologies and emphasize the need for governance frameworks that address not only technical performance but also institutional accountability and transparency in algorithmic systems deployed within law enforcement contexts. • Public attitudes vary significantly across facial recognition applications. • Greater AI knowledge correlates with decreased trust in police facial recognition technology, challenging assumptions that public skepticism stems from ignorance. • Trust in law enforcement institutions is the strongest predictor of acceptance of AI-driven facial recognition, indicating that technology adoption depends on institutional legitimacy rather than technical features. • Findings suggest governance frameworks must prioritize institutional accountability and public participation over purely technical performance metrics.