Computer cops

Computer cops

Inside the big business of selling AI to the police.

I stood before a hulking glass and brick structure in the heart of Fort Worth, Texas. Thousands gathered inside to see what had been billed as “the future of policing in the digital age.” As press, I was prohibited from entering, but from a number of nearby locations, I met with attendees who told me what was being sold within. And 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, which also happen to be critical steps in the legal process. It’s a similar sales pitch to the one that’s been exhaustively broadcast to businesses in recent years: Let the machines handle the busywork, so you can focus on more meaningful tasks. But in law enforcement, the automation of seemingly innocuous “busywork” — like taking the time to carefully fill out a police report or review a suspect’s case history — can have immense consequences on people’s lives.

Among the AI products on offer at the conference’s showroom this May were facial-recognition cameras, automated license plate readers, body cameras, chatbots to field non-emergency 911 calls, gunshot detection platforms, drones, and report-writing tools. As the country has reckoned with law enforcement becoming detached from actual, human police presence in neighborhoods, the industry is continuing to embrace automation.

The decision-making process itself in police departments is increasingly being handed over to algorithms. A legion of tech startups are now selling AI to police as a kind of automated air traffic control system, a centralized digital brain that can process the vast quantities of data now being collected — oftentimes by other surveillance and automation tools sold by those very same companies — and help departments delegate resources accordingly. Even police aren’t necessarily thrilled about these pitches.

“A lot of it is sales gimmicks that don’t actually deliver on what the promise is,” Abrem Ayana, a police captain in Brookhaven, Georgia, told me. In the absence of comprehensive federal oversight or industry standards — and due to the novelty of the tech itself — law enforcement officials like Ayana often have no choice but to take companies’ word that their products are safe and that they work as advertised.

Police departments have used technology for decades to analyze data and, in theory, make more informed decisions in the field. In some notorious cases, it’s completely backfired. CompStat and PredPol (short for “computer comparison statistics” and “predictive policing,” respectively), for example, were two early experiments that sought to mitigate fallible human judgement through the use of supposedly unbiased statistics. Instead, they ended up exacerbating the very problems they were meant to solve. But while those early experiments failed to usher in a new era of unbiased policing as their proponents had hoped, human beings were at least still at the helm, making the most important decisions.

The sales pitch behind this new wave of AI products is that the mistakes of the past were enabled by a lack of objective, real-time data. AI can, in theory, now help to bridge the gap by ramping up the amount of public safety data that’s collected and the level of analysis to which it’s subjected. Many public safety advocacy groups and legal experts, however, warn that an influx of black box algorithms into law enforcement will erode transparency and accountability at a time when much of the public’s trust of the police is already dangerously frayed.

Jason Truppi, a former FBI special agent specializing in cybercrime, told me that police are drowning in a sea of data. Truppi, wearing a pair of Meta Ray-Ban Smart Glasses, spoke quickly and excitedly in sentences peppered with corporate buzzphrases. In late 2020, he cofounded ForceMetrics, a software company offering an “AI-powered decision-assist platform, enabling public safety agencies to increase operational efficiency and better serve their communities in real time,” as described by its LinkedIn page.

All of the record-keeping systems that police departments have been using for the past two decades, from emergency call logs to parole record files to body camera footage databases, have, according to Truppi, created a burdensome information overload. “All the systems of record [used by police departments] are essentially antiquated,” he told me.

“We don’t use the ‘p word’ at all, because it failed.”

ForceMetrics offers police departments a platform called Velocity, which “uses AI to turn overwhelming amounts of public safety data into clear, actionable insights,” according to the company’s website. In police-tech industry-speak, Velocity is what’s known as a real-time crime center, or RTCC. First adopted by the New York City Police Department over 20 years ago, RTCCs are designed to aggregate police data coming in from multiple streams — like 911 dispatch, CCTV cameras, and license-plate scanners — to provide officers with a summary of what to expect when they arrive on a scene. The theory is that the more real-time data you can give officers, the less likely they’ll be to go in “guts and guns,” as Truppi puts it. It’s a cheeky euphemism for when things go bad and people get killed.

In the past, RTCCs were overseen by human analysts whose job was to collect all the incoming digital data, organize it, and send it to the officers on patrol. But as Truppi suggests, the proliferation of new data-collection technologies within policing over the years has made it effectively impossible for any department to stay afloat in the deluge of information. By 2019, the NYPD was collecting around two years’ worth of body camera footage* every week*, according to the transcript of a 2019 Committee on Public Safety hearing — too much for even the most diligent human employee to meaningfully analyze.

Modern RTCCs like Velocity are designed to quickly extract patterns from oceans of data with the goal of improving situational awareness for cops. According to Truppi, the “unfortunate events” that have so disastrously damaged Americans’ trust in police departments in recent years, especially during the pandemic, can largely be attributed to a lack of what he calls “a data-driven approach” to policing.

Nina Loshkajian, a fellow at the New York University Center on Race, Inequality, and the Law, is wary of this claim. “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.”

Truppi’s company is competing with two of the biggest players in the modern police-technology industrial complex: Motorola Solutions and Axon Enterprise, both of which make not only their own RTCCs, but also many of the data-collection and surveillance technologies they rely on.

In early 2024, Axon — originally called TASER — acquired surveillance technology company Fusus to launch a RTCC, which was officially branded as Axon Fusus. By that time, Axon was already a well-known purveyor of stun guns, body-worn cameras, and automated license plate readers. The company also offers a popular AI-powered report-writing tool called Draft One, drones for police departments through a program called Axon Air, and even its own AI chatbot.

Axon and Motorola are part of a very small group of companies competing to effectively monopolize the entire modern police technology stack, from the collection of data at crime scenes to the strategic decision-making capabilities of AI-powered RTCCs. Police departments today o