1. California
California has become a frontrunner in adopting AI surveillance, especially in urban hubs like Los Angeles and San Francisco. Police departments are now leaning heavily on camera networks powered by facial recognition and predictive policing algorithms. The LAPD has experimented with tools like PredPol, which suggests patrol routes based on crime data. While this tech aims to improve efficiency, it’s raised major concerns over racial profiling and privacy.
Despite some pushback, cities like Fresno use systems like ShotSpotter and real-time crime centers to monitor neighborhoods. These tools alert officers only after AI detects a potential threat, reducing the need for routine street patrols. Critics argue this replaces community-based policing with cold, algorithmic judgment. Still, with staffing shortages and high crime concerns, the state’s commitment to AI remains strong.
2. Illinois
In Chicago, the use of AI is becoming more embedded in law enforcement strategies. The CPD’s Strategic Decision Support Centers use gunshot detection tech and predictive analytics to inform where officers should be deployed. While some officers are still physically present, their movements and patrols are often dictated by AI-generated forecasts. This system shifts traditional “walking the beat” duties to a data-led command center.
The city also uses facial recognition tech via a partnership with the Illinois State Police. These tools help with tracking suspects and automating traffic enforcement, further reducing the burden on patrol officers. Illinois has laws regulating facial recognition use, but enforcement is spotty. Still, the infrastructure for AI-driven policing is already woven into day-to-day operations.
3. New York
New York City has embraced AI surveillance in a big way, with its Domain Awareness System (DAS) connecting over 20,000 cameras citywide. Developed by the NYPD and Microsoft, DAS uses analytics to identify suspicious behavior and flag potential threats. It pulls in data from license plate readers, radiation detectors, and more—essentially replacing on-the-ground observation. Officers now respond to alerts rather than patrol blindly.
The system even integrates social media monitoring and predictive analytics. With real-time tracking and data fusion, the NYPD can reassign human patrol units based on digital cues. This has led to criticism about surveillance overreach and lack of transparency. But the city argues it’s necessary for a population of over 8 million people.
4. Texas
Texas has been pouring funds into AI tools for law enforcement, especially along its southern border. The state uses drones, motion sensors, and AI-powered surveillance towers to monitor border activity—essentially doing the work of patrol agents. In cities like Houston and Dallas, police use gunshot detection and real-time camera systems for urban crime monitoring. These technologies shift focus away from proactive patrols to AI-triggered responses.
Police departments argue that with Texas’ sprawling geography, AI is necessary to maintain oversight. The state has also experimented with facial recognition in partnership with federal agencies. These systems are controversial, particularly for how they impact immigrant communities. Still, they continue to expand with limited regulation.
5. Georgia
Atlanta has invested heavily in “Operation Shield,” a network of more than 12,000 cameras monitored by a video integration center. The AI software behind these cameras can flag unusual behavior and direct officers accordingly. This allows for fewer physical patrols and more response-oriented deployment. Many of the cameras are privately owned but feed into the city’s central system.
The city has also piloted predictive policing programs, using crime trends to assign patrol zones. These AI-led approaches have made human presence more reactive than proactive. Some civil rights groups are challenging this system for lack of oversight. But law enforcement says it’s helping them do more with fewer resources.
6. Florida
Florida’s Pasco County Sheriff’s Office has made headlines for its controversial predictive policing program. It used AI to generate watchlists of people it believed were likely to commit crimes, leading to increased surveillance and home visits. While the practice drew lawsuits and investigations, the AI framework behind it remains in use in modified forms. The state has also embraced facial recognition and real-time video monitoring.
In Miami and Tampa, city police departments use camera analytics and automated license plate readers extensively. These systems alert police when suspicious vehicles enter high-crime areas, replacing the need for continuous physical patrols. Civil liberty advocates worry about profiling and false positives. But for departments facing budget constraints, AI is a cost-effective solution.
7. Arizona
Arizona has become a testbed for surveillance drones and autonomous monitoring systems. The Maricopa County Sheriff’s Office uses drones with AI to patrol wide desert areas—essentially doing the job of human deputies. In Phoenix, police use license plate readers and networked cameras to monitor traffic and street crime. These tools create a digital patrol net that operates 24/7.
The state has also experimented with facial recognition through partnerships with federal databases. Though public transparency is limited, internal documents show AI tools are deeply embedded in crime tracking systems. As a result, the physical presence of officers has declined in some areas. Privacy advocates have flagged these trends as ripe for abuse.
8. Washington
Seattle has leaned into AI as a tool for reducing police presence without reducing crime coverage. The city uses predictive analytics and gunshot detection to alert officers only when needed. Real-time surveillance helps cover areas that once required constant patrols. And mobile surveillance trailers are equipped with AI that can identify suspicious activity.
Washington State also supports a statewide network of license plate readers and facial recognition tech. This has enabled automated tracking of vehicle and foot traffic, reducing the burden on human officers. Though the state has considered regulations, much of the tech continues to operate with little public input. Still, departments argue the system improves efficiency and response time.
9. Nevada
Las Vegas has implemented AI surveillance to manage everything from casinos to street crime. The city uses AI-driven camera networks in high-traffic areas to detect unusual behavior or crowd buildups. These systems feed alerts to police, allowing them to respond only when needed. This has drastically reduced routine patrols in popular districts like the Strip.
Beyond city limits, Nevada State Police also use predictive algorithms to assign highway patrol routes. The integration of AI into traffic management and urban policing continues to grow. While tourists may not notice, their movements are tracked and analyzed in real time. The reliance on tech has led some to question where the line between safety and surveillance lies.
10. Massachusetts
Boston and surrounding cities have adopted AI tools for both traffic and crime monitoring. The Boston Police Department uses software to flag high-risk areas and coordinate patrols accordingly. This allows officers to be selectively deployed, rather than keeping them on constant beat duty. Camera systems with facial recognition also play a major role.
Massachusetts has begun debating legal restrictions, but enforcement remains unclear. Police departments continue to expand AI usage in public schools and transportation hubs. Residents are growing more aware of how often they’re being watched. Still, law enforcement insists these tools are essential in a densely populated state.
11. Michigan
Detroit’s Project Green Light links private business cameras to a central police hub, creating a citywide surveillance network. AI monitors the feeds and flags suspicious behavior, prompting a rapid police response. This system has allowed for a reduction in foot patrols in covered areas. Many businesses voluntarily opt in for quicker emergency response.
The city also uses facial recognition software, despite public controversy. Lawmakers have criticized its potential for false matches, especially against people of color. Nevertheless, police continue to use the system as a force multiplier. Patrol officers are often redirected based on AI data instead of fixed beats.
12. Colorado
Denver has been expanding its surveillance reach through AI-driven traffic and safety cameras. These cameras can detect abandoned vehicles, loitering, or even fights breaking out—triggering alerts to law enforcement. Officers now arrive in response to data, not routine sweeps. This shift reflects a growing dependence on AI for situational awareness.
The state also uses drones for rural monitoring, especially in wildfire-prone areas. Law enforcement agencies piggyback on this infrastructure to patrol hard-to-reach zones. While effective, this strategy reduces direct community engagement. Critics argue it creates a sense of remote governance over human safety.
13. Pennsylvania
Philadelphia has quietly implemented AI in coordination with its public transit system. Surveillance cameras on buses, trains, and stations feed into AI systems that detect fights, suspicious packages, or vandalism. This allows police to intervene only when AI raises a red flag. It has reduced the need for stationing officers across the entire transit grid.
Outside of transit, Philly’s camera networks and license plate readers are becoming more integrated with predictive software. Police departments are gradually adopting these tools to guide deployment. While they still keep some foot patrols, the emphasis is on response over presence. It’s part of a broader shift toward tech-led policing across the state.
14. Ohio
Cleveland and Columbus are increasing their use of real-time crime centers, where AI filters video feeds for threats. The systems prioritize alerts and help determine where officers are most needed. This has led to fewer random patrols and more strategic interventions. It’s part of a broader effort to modernize Ohio’s approach to urban policing.
Smaller towns are also adopting automated license plate readers and facial recognition tech. These systems are shared across jurisdictions, extending surveillance without more staff. The shift is driven by budget limitations and rising crime complexity. However, privacy watchdogs are calling for more oversight before expansion continues.
15. Oregon
Portland has been at the center of debates about facial recognition and AI surveillance. While the city officially banned facial recognition in public spaces, private firms and state agencies continue to use it elsewhere. At the state level, Oregon employs AI-powered license plate tracking and predictive analytics to monitor traffic and criminal activity. Patrol routes are now often algorithmically generated.
The paradox is that while Portland resists AI policing in theory, it’s quietly integrating it in practice. Law enforcement agencies outside the city limits use drones and smart cameras in rural areas. This hybrid approach—limiting AI in public view but expanding it operationally—is unique. It shows how even “anti-surveillance” states are making room for AI when convenient.
This post 15 U.S. States Where AI Surveillance Just Replaced Real Police Patrols was first published on American Charm.
