I stood before a hulking glass and brick structure in Fort Worth, Texas, where thousands gathered to see what had been billed as “the future of policing in the digital age.” As press, I was barred from entering, but I met with attendees outside who told me what was being sold inside. I learned that AI is threatening to seize the very heart of policing in America.
The promise of AI at this year’s International Association of Chiefs of Police (IACP) Technology Conference focused on automating routine parts of the job — critical steps in the legal process. It’s a similar pitch to what businesses have heard for years: Let machines handle busywork so you can focus on more meaningful tasks. But in law enforcement, automating seemingly innocuous busywork — like filling out a police report or reviewing a suspect’s case history — can have immense consequences on people’s lives.
Among the AI products on display were facial-recognition cameras, automated license plate readers, body cameras, chatbots for non-emergency 911 calls, gunshot detection platforms, drones, and report-writing tools. As the country has grappled with law enforcement becoming detached from human police presence, the industry continues to embrace automation.
Algorithms Take the Wheel
The decision-making process in police departments is increasingly being handed over to algorithms. A host of tech startups are selling AI as an automated air traffic control system — a centralized digital brain that processes vast quantities of data, often collected by the same companies, and helps departments delegate resources. Even police aren’t thrilled about these pitches.
“A lot of it is sales gimmicks that don’t actually deliver on what the promise is,” said Abrem Ayana, a police captain in Brookhaven, Georgia. Without comprehensive federal oversight or industry standards — and due to the novelty of the tech — officials like Ayana often have to take companies at their word that products are safe and work as advertised.
Police have used technology for decades to analyze data and make more informed decisions. In some notorious cases, it backfired. CompStat and PredPol, early experiments meant to mitigate human bias through statistics, ended up exacerbating the problems they aimed to solve. But while those early efforts failed, humans were still at the helm making the most important decisions.
The new wave of AI products argues that past mistakes stemmed from a lack of objective, real-time data. AI can now bridge that gap by ramping up data collection and analysis. But many public safety advocacy groups and legal experts warn that black box algorithms will erode transparency and accountability at a time when public trust in police is already frayed.
Drowning in Data
Jason Truppi, a former FBI special agent specializing in cybercrime, told me that police are drowning in data. Wearing Meta Ray-Ban Smart Glasses, he spoke quickly, peppering his sentences with corporate buzzphrases. In late 2020, he cofounded ForceMetrics, a company offering an “AI-powered decision-assist platform” for public safety agencies.
All the record-keeping systems police have used for two decades — from emergency call logs to parole files to body camera databases — have created an information overload, according to Truppi. “All the systems of record are essentially antiquated,” he said.
“We don’t use the ‘p word’ at all, because it failed.” — Jason Truppi, cofounder of ForceMetrics
ForceMetrics offers a platform called Velocity, which “uses AI to turn overwhelming amounts of public safety data into clear, actionable insights,” per the company’s website. Velocity is a real-time crime center (RTCC), a concept first adopted by the NYPD over 20 years ago. RTCCs aggregate data from multiple streams — 911 dispatch, CCTV cameras, license-plate scanners — to give officers a summary of what to expect on a scene. The theory is that more real-time data reduces the chance of going in “guts and guns,” as Truppi puts it — a euphemism for when things go bad and people get killed.
In the past, human analysts oversaw RTCCs, collecting and organizing incoming data for officers on patrol. But the proliferation of new data-collection technologies has made it impossible for any department to stay afloat. By 2019, the NYPD was collecting around two years’ worth of body camera footage every week, according to a 2019 Committee on Public Safety hearing — too much for any human to meaningfully analyze.
Modern RTCCs like Velocity extract patterns from oceans of data to improve situational awareness. Truppi says the “unfortunate events” that damaged trust in police, especially during the pandemic, stem from a lack of “a data-driven approach.”
Nina Loshkajian, a fellow at the NYU Center on Race, Inequality, and the Law, is wary. “The reality is that police departments had already been using predictive algorithms, which companies touted as data-driven, for years before calls to defund the police revved up in 2020,” she told me. “These algorithmic systems did not prevent violent encounters between police and civilians then, and we shouldn’t be tricked into thinking they’ll make a meaningful difference in the future.”
Big Players and a Growing Market
Truppi’s company competes with two giants in the police-technology industrial complex: Motorola Solutions and Axon Enterprise. Both make their own RTCCs as well as many of the data-collection and surveillance technologies they rely on. In early 2024, Axon — originally called TASER — acquired surveillance company Fusus to launch its RTCC, Axon Fusus. Axon already sells stun guns, body cameras, license plate readers, an AI report-writing tool called Draft One, drones through Axon Air, and its own AI chatbot.
Axon and Motorola are part of a very small group of vendors dominating this space. As police departments across the U.S. face pressure to modernize, the market for AI policing tools is poised to grow. But without clear regulations, the promise of efficiency may come at the cost of civil liberties. The future of policing will depend on whether these systems can deliver on their promises — or repeat the mistakes of the past.
