AI and Body-Worn Cameras: What Officers Think of Truleo

Estimated reading time: 9 minutes

The 5 Percent Problem: Why Most Body-Worn Camera Footage Goes Unwatched

Body-worn cameras (BWCs) were once seen as a fix-all solution: boosting transparency, increasing accountability for both officers and the public, and rebuilding trust between police and the communities they serve. But recording an encounter is one thing. Reviewing it is another.

And let’s face it, no one became a cop to spend their day watching shaky footage of parking disputes.

Most BWC footage never gets watched. Supervisors typically review videos only when there is a use-of-force incident, a public complaint, or a few selected at random for evaluation. The result is what researchers call the “five percent problem”. Studies have shown that less than 5 percent of BWC footage is ever reviewed. If no one is watching, the promise of accountability quickly starts to fall apart.

This is where AI tools like Truleo come in. These platforms scan all BWC footage and flag both potential issues and strong examples of professional policing, such as clear communication or successful de-escalation. Instead of relying on a human supervisor to catch something in hours of footage, AI can quickly surface what matters most.

But police work is complex. It demands discretion, quick decision-making, and an ability to read subtle cues in tense situations. An algorithm does not know what the officer was feeling, what information they had at the time, or how body language shaped the moment. That raises an important concern: are we heading toward a future where every encounter is second-guessed by a machine? What starts as accountability could easily slide into micromanagement. Before long, your camera might be flagging encounters with notes like, “Sure, they had a machete… but did you ask how they were feeling inside?”

These were the same fears that surrounded the arrival of body-worn cameras themselves. Now, with artificial intelligence reviewing every recorded moment, the question is whether those fears are finally being realized.

Let’s take a look at what the research says.

Enter the Research

A 2025 study by Michael D. White, Aili Malm, Seth Watts, and Genesis B. Navarrete explored how officers responded to Truleo after it was rolled out. The researchers focused on two mid-sized Arizona police departments (Apache Junction and Casa Grande) and followed officer attitudes and usage over a five-month period.

Officers were randomly assigned to either a treatment group, where they and their sergeants had access to Truleo dashboards, or a control group, where neither officers nor supervisors had access. This setup allowed the researchers to assess both exposure and attitudes toward AI-driven BWC analysis.

To track changes over time, officers were surveyed two months before Truleo deployment and again five months after. In addition, weekly and monthly surveys captured how often the dashboards were used by officers and their supervisors.

Here’s what the study revealed:

Officer Acceptance and Use of AI-Driven Body-Worn Camera Footage Review

Police departments review only a fraction of the enormous amount of body-worn camera (BWC) footage recorded daily by their officers. The inability to review BWC footage may undermine many of the perceived benefits of BWCs, most notably increased accountability and civility during officer-community member interactions. Artificial Intelligence (AI) has the potential to overcome this problem through analysis of vast amounts of BWC footage, but AI’s utility hinges on officer acceptance and use of the technology. In the current study, the authors measure attitudes and use of Truleo, an AI-based program that uses natural language processing to deconstruct, analyze, and categorize BWC footage audio. We conducted surveys of officers and supervisors in two medium-sized Arizona police departments both two months before and five months after the departments deployed Truleo via randomized controlled trials. Attitudes about Truleo pre-deployment were mostly neutral, and they remained so after deployment. Post-deployment, officers did report increased understanding of Truleo, and they became less concerned about how the AI platform was being used. In terms of use, officers accessed Truleo 1.6-1.8 times each week, and supervisors averaged 3.6-4.6 times per month. Results suggest AI-driven BWC review can be successfully integrated into a police department’s day-to-day operations.

Key Findings: How Officers Respond to AI in Body-Worn Cameras

1. Officers started neutral and stayed mostly neutral.

Before and after Truleo was deployed, and when compared to officers who did not use it, attitudes remained generally neutral. But that’s not necessarily a bad thing: it means skepticism didn’t grow, and resistance didn’t spike. Officers didn’t love it but they also didn’t hate it. Which, let’s be honest, is high praise for any new technology in law enforcement.

2. Understanding improved, concerns declined.

Over time, officers in the treatment group were significantly more likely to say they understood how Truleo worked and felt less concerned about misuse. Fears that supervisors would “fish” for policy violations or use it solely for discipline decreased after actual experience with the platform.

3. Use was steady and modest.

• Officers accessed their Truleo dashboard about 1.7 times per week.
• Sergeants checked it 3–5 times per month.

This isn’t overwhelming usage, but it shows consistent engagement across the study period.

4. Truleo helped identify areas for improvement.

Officers, especially in Casa Grande, increasingly agreed that Truleo highlighted areas where they could improve, and that its dashboard offered useful information over time.

5. Very few red flags.

In five months:

• Apache Junction: 0 negatively flagged encounters out of 15,818.
• Casa Grande: 2 negatively flagged encounters out of 23,972.

Turns out AI isn’t hunting down every raised eyebrow or snarky tone as misconduct, which may help the system feel less punitive. In fact, it’s arguably less strict than your average field training officer. The exact criteria for what counts as a flaggable offense is a topic for future research.

6. Leadership and communication made a difference.

Both departments had chiefs who were proactive about communicating the purpose of Truleo. They maintained transparency and set expectations early. That mattered. And because the departments were smaller (fewer than 100 officers), internal communication was easier.

Why Does It Matter?

We’ve spent millions on body-worn cameras, yet most of the footage goes unwatched. AI tools might finally bridge this gap.

One of the key takeaways from the study is that concerns about officer resistance and morale may be overstated. Despite initial skepticism, officer attitudes toward AI-based BWC analysis software remained largely neutral before and after deployment. Officers did not report a significant drop in morale, and there was no strong pushback against having their footage analyzed by an automated system.

In fact, some of the initial concerns, such as the belief that the software would be used to punish officers or that it would lead to excessive oversight, actually declined over time, especially among officers who had direct access to the platform. These officers were more likely to understand how the software worked and were less worried about its potential misuse.

Overall, the findings suggest that with clear communication and thoughtful rollout, AI tools for reviewing body-worn camera footage can be adopted without harming morale or trust. While concerns about surveillance and micromanagement are valid, this study shows those fears may not come to life in agencies that maintain transparency and support throughout the process.

How to Implement This in Your Agency

1. Communicate early and often.

Let officers know what the technology does, what it doesn’t do, and how flagged footage will be handled. Be transparent about policies, expectations, and goals. Share real data from pilot programs or other departments. “This agency only had five red flags all year” goes a long way toward building officer confidence. It helps reassure them that the system is not designed to punish minor things like raising your voice.

2. Normalize a learning mindset.

AI BWC review software doesn’t just find misconduct, it highlights professional, respectful, and skillful interactions. Use it to reinforce what’s going well, not just what needs correction. Positive reinforcement matters. If the AI software flags an officer for de-escalating a tense situation, recognize that publicly. Use it to build morale and reinforce values.

3. Define how flags are used.

Make it clear that a red flag isn’t an automatic write-up. Supervisors should review the context, talk to the officer, and determine next steps based on the situation.

4. Start small, monitor closely.

Both agencies in the study were smaller and had strong leadership. That helped. If you’re in a bigger or more complex department, consider a pilot program and monitor how it’s received.

Bottom Line:

AI will not replace human judgment, but it can help agencies unlock the full potential of body-worn cameras. While more research is needed to evaluate how accurately tools like Truleo identify both positive and negative encounters. This study shows that once officers begin using the technology, they are generally not opposed to it, and it can be introduced without harming morale.

AI is not the enemy of good policing. When implemented with care, it can be a tool for fairness, learning, and stronger supervision not just surveillance.

While this post discusses Truleo, it is not an endorsement of the platform. It is simply a look at how one specific AI tool was used in recent research.

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Carlee Ruiz

Carlee Ruiz is the founder of PoliceResearchHub, a platform that turns academic research into practical insights for law enforcement. She brings seven years of experience as a crime analyst and justice researcher, with a focus on evidence based policing, gun violence prevention, and public safety strategy. Carlee currently works as a researcher at National Policing Institute, where she supports evaluations of law enforcement programs and criminal justice initiatives across the country. Through PoliceResearchHub, she aims to make research easier to understand and more useful for the people doing the work on the ground.