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TRUVACE RECORD VERSION
record: TRV-2026-2495
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:42:39.093931Z
status: published
lens: p_space
sector: crime
headline: Reporting crime to police via human and AI live chats: the salience of procedural justice and outcome favourability
dek: 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 out…
gain_title: (none)
problem_title: In an online experiment of 648 participants, identical police behaviour in a simulated burglary report was rated less fair and less satisfactory when delivered by an AI chatbot than by a human officer.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In an online experiment of 648 participants, identical police behaviour in a simulated burglary report was rated less fair and less satisfactory when delivered by an AI chatbot than by a human officer.
problem_evidence: identical behaviour was rated as less fair and less satisfactory when delivered by a chatbot
quick_read: An online experiment with 648 participants found an AI penalty in how people judge police contact. Participants read a simulated exchange in which a burglary was reported by live chat to either a human officer or an AI chatbot. The same behaviour was rated less fair and less satisfactory when the chatbot delivered it.

The result matters as police services adopt automated systems for crime reporting. Perceived fairness and satisfaction are tied to public views of police legitimacy, and the study suggests that handing contact to a chatbot can lower both even when the treatment is identical.

Fair treatment still counted. Procedural justice shaped fairness and satisfaction for both human and AI operators. The experiment used simulated exchanges, so it measures reactions to scenarios rather than real reports.
limitation: The study used simulated written exchanges in an online experiment, so participants evaluated a scenario they read rather than a burglary they reported to police.
tag: Evidence-backed problem
key_points: The online experiment had 648 participants who read a simulated burglary report made through live chat. | Reports went to either a human officer or an AI chatbot. | Researchers varied operator type (human or AI), procedural justice (fair or unfair) and outcome (active or passive). | Participants rated perceived fairness, satisfaction and willingness to use the reporting method. | Procedural justice shaped fairness and satisfaction in both the human and AI conditions. | Identical behaviour was rated less fair and less satisfactory when a chatbot delivered it. | Negative effects of a passive outcome were reduced when the treatment was fair.
rundown: The study, published in Police Practice and Research, tested how people evaluate police–public interactions by live chat. Participants read a simulated exchange in which a burglary was reported to either a human officer or an AI chatbot, and the researchers varied three factors: operator type, procedural justice and outcome.

Participants rated perceived fairness, satisfaction and willingness to use the reporting method. Fair treatment raised fairness and satisfaction ratings in both conditions, but the chatbot was rated lower than the human officer for identical behaviour.

Outcome also influenced responses, comparing active and passive police outcomes. Its negative effects were mitigated by fair treatment. The authors say the findings point to the importance of procedural justice and its attenuation in AI-mediated policing.
sources:
- peer_reviewed | Police Practice and Research | https://doi.org/10.1080/15614263.2026.2741740 | 2026-10-18
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