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It can be tricky for reporters to get past certain doors, and the door to the International Association of Chiefs of Police conference is one that’s almost perpetually shut to the media. Thus, I was pleasantly surprised when I was able to attend for a day in Boston last month. 

It bills itself as the largest gathering of police chiefs in the United States, where leaders from many of the country’s 18,000 police departments and even some from abroad convene for product demos, discussions, parties, and awards. 

I went along to see how artificial intelligence was being discussed, and the message to police chiefs seemed crystal clear: If your department is slow to adopt AI, fix that now. The future of policing will rely on it in all its forms.

In the event’s expo hall, the vendors (of which there were more than 600) offered a glimpse into the ballooning industry of police-tech suppliers. Some had little to do with AI—booths showcased body armor, rifles, and prototypes of police-branded Cybertrucks, and others displayed new types of gloves promising to protect officers from needles during searches. But one needed only to look to where the largest crowds gathered to understand that AI was the major draw. 

The hype focused on three uses of AI in policing. The flashiest was virtual reality, exemplified by the booth from V-Armed, which sells VR systems for officer training. On the expo floor, V-Armed built an arena complete with VR goggles, cameras, and sensors, not unlike the one the company recently installed at the headquarters of the Los Angeles Police Department. Attendees could don goggles and go through training exercises on responding to active shooter situations. Many competitors of V-Armed were also at the expo, selling systems they said were cheaper, more effective, or simpler to maintain. 

The pitch on VR training is that in the long run, it can be cheaper and more engaging to use than training with actors or in a classroom. “If you’re enjoying what you’re doing, you’re more focused and you remember more than when looking at a PDF and nodding your head,” V-Armed CEO Ezra Kraus told me. 

The effectiveness of VR training systems has yet to be fully studied, and they can’t completely replicate the nuanced interactions police have in the real world. AI is not yet great at the soft skills required for interactions with the public. At a different company’s booth, I tried out a VR system focused on deescalation training, in which officers were tasked with calming down an AI character in distress. It suffered from lag and was generally quite awkward—the character’s answers felt overly scripted and programmatic. 

The second focus was on the changing way police departments are collecting and interpreting data. Rather than buying a gunshot detection tool from one company and a license plate reader or drone from another, police departments are increasingly using expanding suites of sensors, cameras, and so on from a handful of leading companies that promise to integrate the data collected and make it useful. 

Police chiefs attended classes on how to build these systems, like one taught by Microsoft and the NYPD about the Domain Awareness System, a web of license plate readers, cameras, and other data sources used to track and monitor crime in New York City. Crowds gathered at massive, high-tech booths from Axon and Flock, both sponsors of the conference. Flock sells a suite of cameras, license plate readers, and drones, offering AI to analyze the data coming in and trigger alerts. These sorts of tools have come in for heavy criticism from civil liberties groups, which see them as an assault on privacy that does little to help the public. 

Finally, as in other industries, AI is also coming for the drudgery of administrative tasks and reporting. Many companies at the expo, including Axon, offer generative AI products to help police officers write their reports. Axon’s offering, called Draft One, ingests footage from body cameras, transcribes it, and creates a first draft of a report for officers. 

“We’ve got this thing on an officer’s body, and it’s recording all sorts of great stuff about the incident,” Bryan Wheeler, a senior vice president at Axon, told me at the expo. “Can we use it to give the officer a head start?”

On the surface, it’s a writing task well suited for AI, which can quickly summarize information and write in a formulaic way. It could also save lots of time officers currently spend on writing reports. But given that AI is prone to “hallucination,” there’s an unavoidable truth: Even if officers are the final authors of their reports, departments adopting these sorts of tools risk injecting errors into some of the most critical documents in the justice system. 

“Police reports are sometimes the only memorialized account of an incident,” wrote Andrew Ferguson, a professor of law at American University, in July in the first law review article about the serious challenges posed by police reports written with AI. “Because criminal cases can take months or years to get to trial, the accuracy of these reports are critically important.” Whether certain details were included or left out can affect the outcomes of everything from bail amounts to verdicts. 

By showing an officer a generated version of a police report, the tools also expose officers to details from their body camera recordings before they complete their report, a document intended to capture the officer’s memory of the incident. That poses a problem. 

“The police certainly would never show video to a bystander eyewitness before they ask the eyewitness about what took place, as that would just be investigatory malpractice,” says Jay Stanley, a senior policy analyst with the ACLU Speech, Privacy, and Technology Project, who will soon publish work on the subject. 

A spokesperson for Axon says this concern “isn’t reflective of how the tool is intended to work,” and that Draft One has robust features to make sure officers read the reports closely, add their own information, and edit the reports for accuracy before submitting them.

My biggest takeaway from the conference was simply that the way US police are adopting AI is inherently chaotic. There is no one agency governing how they use the technology, and the roughly 18,000 police departments in the United States—the precise figure is not even known—have remarkably high levels of autonomy to decide which AI tools they’ll buy and deploy. The police-tech companies that serve them will build the tools police departments find attractive, and it’s unclear if anyone will draw proper boundaries for ethics, privacy, and accuracy. 

That will only be made more apparent in an upcoming Trump administration. In a policing agenda released last year during his campaign, Trump encouraged more aggressive tactics like “stop and frisk,” deeper cooperation with immigration agencies, and increased liability protection for officers accused of wrongdoing. The Biden administration is now reportedly attempting to lock in some of its proposed policing reforms before January. 

Without federal regulation on how police departments can and cannot use AI, the lines will be drawn by departments and police-tech companies themselves.

“Ultimately, these are for-profit companies, and their customers are law enforcement,” says Stanley. “They do what their customers want, in the absence of some very large countervailing threat to their business model.”

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