Information of high evidentiary quality plays a crucial role in forensic investigations. Research shows that information provided by witnesses and victims often provide major leads to an inquiry.As such, statements should be obtained in the shortest possible time following an incident. However, this is not achieved in many incidents due to demands on resources. This intersectional study examined the effectiveness of a chatbot (the AICI), that uses artificial intelligence (AI) and a cognitive interview (CI) to help record statements following an incident. After viewing a sexual harassment video, the present study tested recall accuracy in participants using AICI compared to other tools (i.e., Free Recall, CI Questionnaire, and CI Basic Chatbot). Measuring correct items (including descriptive items) and incorrect items (errors and confabulations), it was found that the AI CI elicited more accurate information than the other tools. The implications on society include AI CI provides an alternative means of effectively and efficiently recording high-quality evidential statements from victims and witnesses.

Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15–20 min delay either (i) viewed a mock social media site including the culprit, (ii) viewed a mock social media site including a lookalike, or (iii) completed a filler task. A week later, participants made an identification from a photo lineup. It was predicted that searching for a culprit on social media containing the lookalike (rather than the culprit) would reduce lineup identification accuracy. There was a significant association between social media exposure and lineup accuracy for the Target Present lineup (30% more of the participants who saw the lookalike on social media failed to positively identify the culprit than participants in the other conditions), but for the Target Absent lineup (which also included the lookalike) there was no significant association with lineup identification accuracy. The results suggest that if an eyewitness sees a lookalike (where they are expecting to see the culprit) when conducting a self-directed search on social media, they are less likely to subsequently identify the culprit in the formal ID procedure.

Perceptions of police trustworthiness are linked to citizens' willingness to cooperate with police. Trust can be fostered by introducing accountability mechanisms, or by increasing a shared police/citizen identity, both which can be achieved digitally. Digital mechanisms can also be designed to safeguard, engage, reassure, inform, and empower diverse communities. We systematically scoped 240 existing online citizen-police and relevant third-party communication apps, to examine whether they sought to meet community needs and policing visions. We found that 82% required registration or login details, 55% of those with a reporting mechanism allowed for anonymous reporting, and 10% provided an understandable privacy policy. Police apps were more likely to seek to reassure, safeguard and inform users, while third-party apps were more likely to seek to empower users. As poorly designed apps risk amplifying mistrust and undermining policing efforts, we suggest 12 design considerations to help ensure the development of high quality/fit for purpose Police/ Citizen apps.

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