Police and public agencies use facial recognition, license-plate readers, gunshot-detection sensors, camera networks, predictive analytics and drones for different public-safety tasks. Nine documented deployments show what these systems can do operationally—and why alerts, arrests or detected incidents are not by themselves proof that surveillance reduces crime.
What counts as AI surveillance in public safety?
The label covers different tools, and not every camera network uses AI. Some systems search for a match against a watchlist; others detect or locate an event, send information to dispatchers, or help officers document a scene. Their inputs, human checks and evidence of benefit differ, so they should not be treated as one technology.
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- Live facial recognition (LFR): compares faces captured by cameras with a watchlist to generate possible matches.
- Automatic license-plate recognition (ALPR): reads vehicle plates so they can be checked against lists, such as wanted-vehicle records.
- Gunshot-detection technology (GDT): uses acoustic sensors to identify and locate sounds classified as gunfire, then alerts police.
- Video analytics and camera networks: connect public cameras and, in some systems, use analytics or sensor alerts to direct attention to a scene.
- Drones: provide an aerial view or support search, rescue and communication; a drone program may be remotely operated or use autonomous flight functions.
- Predictive analytics: analyzes data to help guide where agencies direct resources. It does not establish that a person will commit a crime.
These systems can be combined. That makes them potentially more useful as operational infrastructure, but also makes it harder to determine which component produced an outcome.
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The examples below cover nine deployments or city programs documented by government and justice-research sources. They are a selected set, not a complete global census. The findings range from reported arrests and alert activity to program descriptions; they do not all measure crime reduction.
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1. London: live facial recognition
The UK government reported that London LFR deployments from January 2024 through September 2025 led to more than 1,300 arrests for a range of serious offenses. It describes the operations as intelligence-led, targeted, geographically limited and time-bound, with public notices posted online and signage used at deployment locations. The government says an alert does not itself determine enforcement: an officer decides what action, if any, to take.
The arrest figure is an operational count, not a count of unique people, convictions or crimes prevented. Legal history also matters: 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 government says subsequent College of Policing guidance and independent National Physical Laboratory testing addressed aspects of the framework and the algorithm used by South Wales Police and the Metropolitan Police. That account should not be generalized into a finding about every system or deployment.
2. New York City: Drone as First Responder
A November 2024 NYC Mayor’s Office announcement described ten drones positioned across five NYPD commands for a Drone as First Responder program. The announced plan was to fly remotely and autonomously to selected priority calls, including missing-person searches and ShotSpotter alerts. The announcement describes intended capabilities; it does not establish the fleet’s current size or independently demonstrate a response-time improvement.
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The same announcement describes broader NYPD drone uses dating to late 2018, including documenting scenes, search and rescue, and communicating with people in barricaded situations. Those uses are distinct from sending a drone to a selected call as a first responder.
3. Milwaukee: public surveillance camera network
A 2024 National Institute of Justice technical summary describes Milwaukee Police Department efforts to optimize its public surveillance network. The network included ALPR cameras and high-definition cameras connected to gunshot detection. The project examined processes, impact and cost-effectiveness, making it an example of an integrated system—not evidence that any single camera type or sensor caused an outcome on its own.
4. Denver: gunshot detection
A July 2024 Office of Justice Programs study by Daniel S. Lawrence and Kenneth J. Novak examined gunshot-detection technology in Denver, Milwaukee and Richmond, drawing on 46 stakeholder interviews. In all three departments, alerts doubled after implementation and staff time allocated to responding increased two- to threefold. For specific events, response was generally quicker than for traditional calls reporting shots heard. The time savings primarily occurred between notification and assignment to an officer, rather than during the officer’s travel after assignment.
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5. Milwaukee: gunshot detection
Milwaukee was also one of the three cities in the Lawrence and Novak study. Its results contribute to the reported pattern of doubled alerts and two- to threefold increases in staff response time across the three departments. The study’s distinction between faster alert-to-assignment intervals and officer travel time is important: a sensor can change how quickly a call is routed without making an officer arrive sooner after being dispatched.
6. Richmond: gunshot detection
Richmond was the third city in the same 2024 evaluation. As in Denver and Milwaukee, the study found more alerts and more staff time devoted to responses after implementation. These are measures of system activity and workload, not a demonstration that shootings or overall crime fell because of the technology.
7. Kansas City: gunshot detection
A 2023 National Institute of Justice-sponsored evaluation reports that Kansas City installed ShotSpotter in 2012. It says the system covered approximately 3.5 square miles and recorded 11,517 detected gunfire incidents through December 31, 2019. A detection count shows what the system logged during that period; it does not establish how many incidents would otherwise have gone unreported or whether gun violence was prevented.
8. Chicago: gunshot detection
The same evaluation reports that Chicago installed ShotSpotter in 2012, expanding from an initial area of approximately 3 square miles to around 100 square miles after later district expansions. It recorded 85,572 detections from February 6, 2017, through December 31, 2019. The evaluation cites an annual subscription cost of $65,000–$90,000 per square mile as reported by ShotSpotter at the time; this is a historical study-period figure, not current pricing.
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9. Minneapolis: integrated surveillance and predictive analytics
A February 2012 National Institute of Justice article describes Minneapolis using crime mapping and predictive analytics to direct resources, alongside approximately 200 cameras, mobile cameras and license-plate recognition. The city also integrated systems so that a gunshot could cause a camera to turn toward the sound. The article reports a long-term decline in Part I offenses beginning in 1998, but describes multiple technologies and organizational changes over that period. The trend therefore cannot be attributed to the technology bundle alone.
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Do these systems reduce crime or improve safety?
The evidence supports a more careful answer than a simple yes or no. Some deployments have documented operational outputs: London reported arrests; gunshot-detection studies measured alerts, workload and response intervals; and city systems have recorded detections or connected sensors to cameras. Those outcomes show that a system was used and produced activity. They do not, by themselves, show a causal reduction in crime, harm or response time overall.
The Government Accountability Office (GAO) noted in its April 2025 smart-cities assessment that it can be difficult to attribute benefits when cities use multiple technologies. It also said scaling technology citywide can be difficult and that gunshot detection could displace gun crime to another area rather than reduce it. An evaluation should therefore distinguish among:
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- System output: alerts, plate reads, possible matches or detected gunfire.
- Operational response: whether an alert is verified, assigned and acted on, and how long each stage takes.
- Public-safety outcome: whether the deployment reduces victimization or harm compared with what would have happened without it.
The 2024 gunshot-detection study provides a useful example of why the distinction matters: alert-to-assignment intervals were generally faster, while officer travel time after assignment was not where the primary savings occurred.
How common are these tools?
Adoption varies by technology and location. A University of Michigan Center for Local, State, and Urban Policy report published in April 2025, based on the Spring 2024 Michigan Public Policy Survey, found that among Michigan jurisdictions funding police or sheriff departments, reported use of public surveillance cameras rose from 28% in 2015 to 50% in 2024. Reported drone use rose from 3% to 31% over the same period.
In that survey, 26% of Michigan sheriffs and police chiefs said their agency used ALPR, 10% said it used facial recognition and 3% said it used AI or predictive tools. These are Michigan findings, not national adoption rates. The survey also found that 55% of local government officials were unsure whether automated assessments were more or less accurate than human assessments.
What safeguards and oversight should the public look for?
Surveillance can expose personal information, produce mistaken or biased results, or be used beyond its original purpose. Safeguards need to address the entire process—from data collection to an officer’s decision—not just the accuracy of a sensor or algorithm.
- Clear purpose and limits: Which calls, locations, watchlists or investigations qualify, and who can authorize use?
- Public notice: Are deployments announced, and can residents find out where and when they operate?
- Human review: Who checks an alert before it prompts police action? In the UK government’s account of London LFR, an officer makes the decision after a possible alert.
- Privacy and data controls: What information is retained, for how long, who can access it, and how is misuse detected?
- Bias assessment and independent evaluation: Are error rates and impacts assessed across affected groups, and are findings made public?
- Outcome measures: Does an evaluation test for changes in safety, rather than reporting only system activity or arrests?
In a December 2024 review, GAO 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 third-party data access. 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 reviewed federal agencies, not every local police department.
GAO’s April 2025 assessment also highlighted transparency and risks of misuse of personal data in smart-city policing. Those concerns make public policies, access controls and independent scrutiny important parts of evaluating a deployment—not optional extras after installation.
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