Reporting crime to police via human and AI live chats: the salience of procedural justice and outcome favourability

As police services adopt automated systems for crime reporting, questions arise about how these technologies affect public perceptions of fairness and legitimacy. This study examines how people evaluate police–public interactions conducted via human or AI-operated live chat. In an online experiment (N = 648), participants read a simulated exchange in which a burglary was reported to either a human officer or an AI chatbot. We manipulated operator type (human vs. AI), procedural justice (fair vs. unfair), and outcome (active vs. passive). Participants rated perceived fairness, satisfaction, and willingness to use the reporting method. Procedural justice shaped fairness and satisfaction in both conditions. However, there was an AI penalty: identical behaviour was rated as less fair and less satisfactory when delivered by a chatbot. Outcome influenced responses, although its negative effects were mitigated by fair treatment. These findings highlight the importance of procedural justice and its attenuation in AI-mediated policing contexts.