Scholarly works

Abstract Research Summary Over time, the evidence on the impact of police body‐worn cameras (BWCs) has become increasingly mixed. Though there are numerous contributors to the inconsistent findings (e.g., context, policy, training, culture), one pressing issue involves the failure of law enforcement agencies to review any more than a fraction of the BWC footage generated by their officers. This footage review problem violates a central precondition for the hypothesized civilizing effect of BWCs on officers: their belief that footage will be reviewed. AI‐driven BWC analytics has emerged as a solution that may overcome the footage review problem, but there has been virtually no research on such AI‐based platforms. The current study fills this gap through an evaluation of Truleo in the Apache Junction and Casa Grande Police Departments (AJPD, CGPD). Officers were randomized to Treatment (Truleo) and Control (non‐Truleo) conditions for a 6‐month period, and we compared study groups across a range of measures including Truleo‐generated professionalism, use of force, complaints, stops, arrests, and citations. Treatment officers in both departments were more likely to generate High Professionalism ratings, but group differences fell short of statistical significance. Use of force decreased significantly in AJPD and in some of the pooled models, though the pooled models were sensitive to multiple‐comparisons corrections. The Bayesian probabilities paradigm offers an additional lens for considering the mixed effects with High Professionalism and use of force. Policy Implications Taken together, results suggest Truleo may hold promise for promoting positive behavior change among officers consistent with a civilizing effect. Law enforcement agencies should explore whether AI‐driven BWC analytics can enhance their BWC program, expand their review of footage, and positively shape their officers’ behavior. Additional research is needed in other settings to test the durability of these findings and the value of AI‐generated metrics of police performance (e.g., professionalism). Researchers should consider the significance of research findings from a broader lens that accounts for practitioner perspectives on the meaningful impact of an innovation. Lastly, the results presented here inform the ongoing dialogue over the role of AI in policing.

Abstract The diffusion of innovations in policing has often been hindered by barriers to implementation and officer acceptance, which can derail an innovation regardless of its validity or effectiveness. Implementation Science (IS) is a useful lens for addressing such concerns because IS empirically examines the way a new technology or strategy is deployed, and it offers insights on barriers, facilitators, and fidelity. The present study uses an IS framework to investigate one of the latest innovations in policing: Artificial Intelligence (AI). In early 2024, two Arizona police departments deployed Truleo, an AI-driven body-worn camera review platform, via randomized controlled trials, but only one of those departments used a feature of Truleo that sends automated positive feedback emails to officers when they engage in behaviors assessed as “highly professional” by the AI algorithm. Using cross-sectional survey data from line-level officers ( n = 84), we estimate both intent-to-treat and instrumental variable regression models to examine the effect of the automated emails on three implementation outcomes: acceptability, appropriateness, and feasibility. Officers in the department with automated feedback enabled reported higher levels of the appropriateness of Truleo. The instrumental variable models suggest that the automated emails resulted in higher levels of acceptability and appropriateness. The findings highlight: (1) the importance of evaluating different implementation strategies when deploying new technologies like AI, and (2) the potential value of providing AI-generated positive feedback to officers in the field as a means of ensuring the successful implementation of AI-driven officer accountability platforms in an agency.

Abstract Body-worn cameras (BWCs) have been widely adopted as a tool to promote police reform. However, studies have shown that most of the footage recorded by BWCs (about 95 per cent) is never reviewed or seen, which undercuts the core benefits of BWCs. Artificial Intelligence-driven analytics may overcome this problem. One example is Truleo, which uses natural language processing to analyse the audio footage and produce metrics of risk and professionalism. However, there is virtually no research examining its use and impact in policing. In the current study, we describe preliminary findings from ongoing randomized controlled trials testing the implementation and impact of Truleo in the Apache Junction and Casa Grande Police Departments. We draw on focus groups of officers, sergeants, and upper-level management and officer perceptions via surveys administered pre-deployment of the technology. We conclude with a discussion of policy implications related to the use of AI for BWC footage review, particularly with regard to how the technology may promote professionalism, intensify supervision, and increase organizational efficiency.

PurposeUnlike protests against police brutality in the past (2014 and earlier), police officers responding to First Amendment-protected demonstrations in summer 2020 likely were wearing body-worn cameras(BWCs). This study seeks to understand police perceptions of the effects of BWCs when used in the George Floyd protests.Design/methodology/approachThe authors use survey data from 100 agencies with federally-funded BWCs to assess the prevalence of BWC deployment to George Floyd protests and perceived benefits and limitations of the technology within this unique context.FindingsAbout three-quarters of agencies encountered some level of demonstration/protest related to the killing of George Floyd, and the majority of those deployed BWCs during these demonstrations. Respondents indicated evidentiary value of footage was a key reason for doing so, and at least three preconditions for a civilizing effect were present.Originality/valueResearch has documented numerous benefits associated with BWCs, from reductions in use of force and citizen complaints to evidentiary value. However, the extent to which BWC benefits extend to public protests is unclear. The George Floyd protests represent an opportunity to understand the prevalence and usefulness of BWCs in policing public protests.

Objectives. To investigate what transpires at opioid overdoses where police administer naloxone and to identify the frequency with which concerns about police-administered naloxone are observed. Methods. We reviewed body-worn camera (BWC) footage of all incidents where a Tempe, Arizona police officer administered naloxone or was present when the Tempe Fire Medical Rescue (TFMR) administered it, from February 3, 2020 to May 7, 2021 (n = 168). We devised a detailed coding instrument and employed univariate and bivariate analysis to examine the frequency of concerns regarding police-administered naloxone. Results. Police arrived on scene before the TFMR in 73.7% of cases. In 88.6% of calls the individual was unconscious when police arrived, but 94.6% survived the overdose. The primary concerns about police-administered naloxone were rarely observed. There were no cases of improper naloxone administration or accidental opioid exposure to an officer. Aggression toward police from an overdose survivor rarely occurred (3.6%), and arrests of survivors (3.6%) and others on scene (1.2%) were infrequent. Conclusions. BWC footage provides a unique window into opioid overdoses. In Tempe, the concerns over police-administered naloxone are overstated. If results are similar elsewhere, those concerns are barriers that must be removed. (Am J Public Health. 2022;112(9):1326–1332. https://doi.org/10.2105/AJPH.2022.306918 )

The authors evaluated the effects of customized police de-escalation training in Tempe, AZ, by comparing officers who did, or did not, receive the training. This training was designed with interpersonal skills, emotional regulation, tactical skills, and officer safety/wellness as key pillars, along with scenario-based assessments and refresher training. The authors investigated the impact of the training using administrative data, as well as body-worn camera (BWC) footage for all use of force incidents six months pre- and post-training. The key outcomes were use of force (overall and by type), encounter length, and citizen and officer injury. Both officer groups saw declines in use of force encounters after training delivery, which is likely attributed to reduced police-citizen contacts due to the global pandemic. Trained officers decreased certain force types that are likely to cause injury (e.g., strikes). Trained officers also spent significantly more time on scene for use of force encounters. Finally, trained officers were less likely to injure community members when using force. These changes occurred with no increased risk of injury to officers who received the de-escalation training.

Police de-escalation training in Tempe, Arizona was evaluated, which aimed to equip officers with enhanced interpersonal and tactical skills. The evaluation was done by providing some officers with training, but not others. By conducting phone surveys of community members who interacted with the police and analyzing body-worn camera (BWC) footage, the authors were able to examine how the training influenced officer behaviour during routine, non-force encounters. The phone surveys showed that officers who completed the training were more likely to use procedural justice, emotional regulation, and de-escalation; they were also more likely to leave community members satisfied with their treatment by officers and with the resolution of the encounter. Results from BWC footage analysis were mixed but useful for understanding the complexities of police-community member encounters. Given that use of force is a rare event in police-community encounters, it cannot be the only outcome assessed in evaluations of de-escalation training. A comprehensive assessment of deescalation training should consider several outcomes, and researchers should investigate training effects in routine encounters that do not involve the use of force

Body-worn camera (BWC) footage was analyzed to describe the characteristics of opioid overdose incidents in which police officers responded in Tempe, Arizona. Using the footage, several concerns raised about police responses to opioid overdoses were investigated. The results indicate that police arrived on scene before Tempe Fire and Medical Rescue (TFMR) around 75% of the time. Most frequently, the individual on scene was unconscious when police arrived but regained consciousness after receiving naloxone. Concerns such as citizen aggression or arrest were rarely observed. Likewise, other concerns such as accidental opioid exposure to an officer and improper administration of naloxone were not observed. The concerns surrounding police involvement in opioid overdose incidents were rarely documented in Tempe, Arizona. If these findings are replicated in other jurisdictions, the concerns over police involvement at opioid overdoses should be reconsidered.