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Police and public agencies use surveillance technologies to identify people or vehicles, detect gunfire, send drones to calls, and help direct officers. Some systems generate useful alerts or have been associated with arrests, but those results do not by themselves show that a technology reduces crime. Here are nine documented examples and what the available evidence can—and cannot—establish.

What counts as AI surveillance in public safety?

The term covers unlike tools, and not every camera network is an AI system. Live facial recognition compares faces with reference images; automatic license plate readers (ALPRs) read plates for comparison with vehicle lists; acoustic gunshot-detection technology (GDT) flags sounds classified as possible gunfire. Video analytics process camera footage, while drones provide aerial views or reach selected calls. Predictive analytics use data to inform where agencies direct resources.

These tools have different inputs and purposes. An alert, match, or prediction is an operational output—not proof that a person committed an offense or that a system made the public safer. The examples below cover documented deployments; they are not a complete global inventory or a claim to reproduce every entry in any one nine-item list.

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How are these systems being used? Nine documented deployments

Deployment Technology and role What the record establishes
London, United Kingdom Live facial recognition (LFR) in police operations The UK government describes targeted, time-limited deployments and reports more than 1,300 arrests from January 2024 through September 2025.
New York City Drone as First Responder (DFR) A November 2024 Mayor’s Office announcement described ten drones across five NYPD commands, intended to fly remotely and autonomously to selected priority calls.
Milwaukee, Wisconsin: public surveillance network ALPR and high-definition cameras connected to gunshot detection A 2024 National Institute of Justice (NIJ) technical summary describes the police department’s effort to optimize an integrated network and evaluate its processes, impact, and cost-effectiveness.
Denver, Colorado: gunshot detection Acoustic alerts intended to identify possible gunfire A 2024 study examined GDT operations and police response in Denver, Milwaukee, and Richmond.
Milwaukee, Wisconsin: gunshot detection Acoustic alerts intended to identify possible gunfire The same three-city study included Milwaukee; its findings apply to the study’s GDT operations, not automatically to every component of the city’s wider camera network.
Richmond, Virginia: gunshot detection Acoustic alerts intended to identify possible gunfire The same 2024 study compared the department’s alert and response processes with those in Denver and Milwaukee.
Kansas City, Missouri Gunshot detection An NIJ-sponsored 2023 evaluation examined the system installed in 2012 and reported 11,517 detected gunfire incidents through December 31, 2019.
Chicago, Illinois Gunshot detection An NIJ-sponsored 2023 evaluation examined a system installed in 2012 and reported 85,572 detections from February 6, 2017, through December 31, 2019.
Minneapolis, Minnesota Integrated policing technologies, including cameras, license plate recognition, gunshot detection, and predictive analytics An NIJ article from 2012 describes crime mapping to direct resources and a system in which a gunshot could cause a camera to turn toward the sound.

How is facial recognition used by police?

London’s LFR operations compare faces captured in a deployment area with reference images. The UK government says deployments are intelligence-led, targeted to limited locations, and time-bound; it also describes public notice through signage and online notices. A possible match does not itself determine enforcement: the government says an officer at the scene decides what action, if any, to take.

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The government reports more than 1,300 arrests associated with London deployments between January 2024 and September 2025. That is an arrest count, not a count of unique people, convictions, or crimes prevented. The figure is an official reported outcome, not an independent estimate of the technology’s effect on crime.

There is also a significant legal history. In the Bridges case, the Court of Appeal found that South Wales Police had not fully complied with privacy, data-protection, and equality laws during two pilots. The UK government says subsequent College of Policing guidance and independent National Physical Laboratory testing addressed aspects of the framework and the algorithms used by South Wales Police and the Metropolitan Police. That account does not establish that every later deployment or system is lawful in every circumstance.

What is gunshot detection technology?

GDT uses acoustic sensors and analysis to flag sounds classified as possible gunfire and notify police. It can surface an incident that has not yet been reported by a caller, but a detection is not a confirmed shooting, and a count of detections is not a count of shootings prevented.

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What the three-city response study found

Daniel S. Lawrence and Kenneth J. Novak’s July 2024 Office of Justice Programs study drew on 46 stakeholder interviews in Denver, Milwaukee, and Richmond. After GDT implementation, each department experienced a twofold increase in alerts and a two- to threefold increase in staff time allocated to responding. For specific events, response times were generally quicker than for traditional calls reporting shots heard. The time savings occurred mainly between notification and officer assignment—not in officers’ travel time after assignment. This points to a potential dispatch advantage alongside increased workload, rather than proof of improved safety.

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What Kansas City and Chicago detection counts mean

The NIJ-sponsored 2023 evaluation reported 11,517 detected gunfire incidents in Kansas City through December 31, 2019, and 85,572 detections in Chicago from February 6, 2017, through December 31, 2019. The study reported that Kansas City’s system covered approximately 3.5 square miles; Chicago expanded from an initial area of approximately 3 square miles to around 100 square miles after later district expansions. The counts describe detections during the stated periods, not verified incidents, prevented shootings, or a causal reduction in gun violence.

The evaluation also reported then-current annual subscription costs of $65,000–$90,000 per square mile, citing the provider. Those are study-period figures, not current pricing.

Do police drones reduce response times?

The available New York City announcement describes a planned capability, not a measured result. In November 2024, the Mayor’s Office said ten drones were positioned across five NYPD commands and intended to fly remotely and autonomously to selected priority calls, including missing-person searches and ShotSpotter alerts. The announcement does not establish the program’s current fleet size, actual response-time improvements, or results from an independent evaluation.

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The NYPD’s broader drone use, described in the same announcement as dating to late 2018, includes scene documentation, search and rescue, and communication with people in barricaded situations. Those functions should not be conflated with the specific DFR program.

What does the Minneapolis example show—and not show?

An NIJ article from February 2012 describes Minneapolis combining crime mapping and predictive analytics for resource direction with approximately 200 cameras, mobile cameras, license plate recognition, and a link that could turn a camera toward a detected gunshot. The article also reports a long-term decline in Part I offenses beginning in 1998. Because that period involved multiple technologies and organizational changes, the reported trend does not establish that the technology bundle caused the decline.

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What do adoption figures say about use?

Use varies by technology and jurisdiction. The University of Michigan’s Center for Local, State, and Urban Policy reported in April 2025 on its Spring 2024 Michigan Public Policy Survey. Among Michigan jurisdictions funding police or sheriff departments, reported public surveillance camera use rose from 28% in 2015 to 50% in 2024, while drone use rose from 3% to 31%. Among Michigan sheriffs and police chiefs, 26% said their agency used ALPR, 10% facial recognition, and 3% AI or predictive-policing tools. These are Michigan survey results, not national adoption rates.

The survey also found uncertainty: 55% of local government officials said they were unsure whether automated assessments were more or less accurate than human assessments. That uncertainty matters when agencies consider systems that may influence deployment decisions.

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When do surveillance outputs amount to public-safety benefits?

Evidence should be read in layers. A sensor detection or facial-recognition alert shows that a system generated an output. Faster assignment or an arrest records an operational result. Neither alone demonstrates fewer crimes, fewer injuries, or a net improvement in safety. Establishing those broader outcomes requires evaluation that can distinguish the technology’s effect from other changes in policing and the surrounding community.

In its April 30, 2025 smart-cities assessment, the U.S. Government Accountability Office (GAO) noted that agencies use ALPRs to compare plates with wanted-vehicle lists and acoustic systems to locate gunfire. It cautioned that benefits are difficult to attribute when cities use multiple technologies, citywide scaling can be difficult, and gunshot detection could shift gun crime to another area rather than reduce it.

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What safeguards and oversight should the public look for?

Surveillance can affect privacy, fairness, and how people are treated, as well as agency operations. A meaningful assessment should ask what data a system collects, how long it is kept, who can access it, how alerts are verified, and whether the agency measures errors and disparate impacts.

  • Purpose and limits: Is use restricted to defined situations, locations, and time periods?
  • Human review: Who checks a match or alert, and who makes the consequential decision?
  • Transparency: Are residents told where and how the technology is used, and can they find public policies and evaluations?
  • Privacy and access: Are retention, sharing, and access rules clear and enforced?
  • Independent evaluation: Does the evaluation distinguish alerts and arrests from changes in crime, harm, or response outcomes?
  • Bias assessment: Does the agency assess risks for the specific technology and its actual use?

A December 3, 2024 GAO review found that selected Department of Homeland Security law-enforcement agencies reported using more than 20 detection, observation, and monitoring technologies in fiscal year 2023, including facial recognition, ALPR, drones, pole-mounted cameras, and access to third-party data. GAO found that reviewed policies did not always address key privacy protections and that agency-wide procedures for assessing bias risks were incomplete. This is a finding about the selected federal agencies GAO reviewed, not every local police department.

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How to compare a deployment responsibly

Rather than treating “AI surveillance” as one product category, compare each system on the questions that match its function:

  • Input: Does it analyze faces, vehicle plates, sounds, video, or aerial views?
  • Purpose: Is it intended to identify a person or vehicle, flag possible gunfire, locate an incident, triage a call, document a scene, or guide deployment?
  • Decision path: What prompts an alert, who verifies it, and what decisions remain with officers?
  • Outcome evidence: Are reported results detections, faster assignment, arrests, or a measured change in safety?
  • Governance: Are notice, retention, access controls, privacy protections, bias assessment, and public oversight documented?

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