Colorado Grandma Keeps Getting Pulled Over Because Police Cameras Cannot Tell the Difference Between a Zero and the Letter O

A 76-year-old woman in Colorado did not expect her golden years to involve repeated run-ins with law enforcement. Yet here she is, getting pulled over again and again, not for speeding, not for running red lights, but because of a one-character error tied to a police database. That tiny typo has turned her ordinary drives into recurring traffic stops, and she has no easy way to stop it.

If this sounds familiar, it should. We covered a nearly identical case in Cherry Hills that has been making the rounds, where a Colorado driver was repeatedly pulled over after Flock Safety alerts flagged his vehicle incorrectly.

The culprit is a system from Flock Safety, which uses automated license plate readers to alert officers when a vehicle of interest is spotted in the area. It is designed to catch stolen cars and flag plates associated with criminal activity. In her case, it has flagged her vehicle as having stolen plates, which is incorrect. Every time she drives through certain areas of Colorado, the cameras pick up her plate, run it against the database, and send officers out to pull her over.

The mistake is not hers. A suspect’s license plate was entered incorrectly—mixing up a zero and the letter O—and her completely valid plate now matches that bad entry in the system. To be clear, her plate is correct. The error exists in the database. The camera reads her plate correctly, matches it to the incorrect entry, and flags her as a suspect every single time.

What Are Flock Safety Cameras and How Do They Work?

Flock Safety is a private company that provides automated license plate reader technology to police departments across the country. The cameras are typically mounted in fixed locations, and they capture images of passing vehicles and their plates. When a plate matches one flagged in a database, an alert is sent to law enforcement in real time.

The system is widely used because it allows departments to extend their reach without putting more officers on the street. One camera can theoretically monitor thousands of vehicles per day. For legitimate cases involving stolen cars or vehicles connected to serious crimes, the technology can be a useful tool. The problem arises when the underlying database contains errors, because the camera does not know the difference between a typo and the truth.

It simply reads the plate, checks the list, and sends the alert.

She Is Not the Only One: A Pattern of Database Errors in Colorado

What makes this story particularly troubling is that it is not a one-off glitch. After the Cherry Hills story aired, multiple Colorado drivers reached out to say the same thing was happening to them. All of them had been wrongly entered into a police database. None of them had been given a clear process for getting removed.

That is a significant problem. If these errors are widespread enough that a single news segment prompted multiple people to come forward, the question becomes how many people are silently dealing with the same issue without knowing they have any options at all. The common thread is not the drivers—it is the database. Each case traces back to incorrect plate entries that the system continues to treat as valid.

A 76-year-old grandmother should not need to call a TV station to get a database corrected.

The public reaction has been pointed. One commenter responding to news coverage of the story wrote that the state of Colorado is allowing innocent people to be treated like criminals, and asked directly who would be held responsible for compensating people for their wasted time. Another observation from the public response: the company providing this service, in their view, should be ashamed for what amounts to a coordinated system-level error targeting people who did nothing wrong.

What Can We Learn from This Situation?

There are a few clear takeaways here that go beyond one grandmother’s frustrating afternoon. First, automated systems are only as reliable as the data they run on. Flock cameras do not make errors in the traditional sense. They do exactly what they are programmed to do. The errors happen upstream, in the human process of entering information into databases, and the cameras simply amplify and repeat those errors at scale.

Second, there is apparently no streamlined process for getting incorrect information removed. If there were, these drivers would have used it and moved on. The fact that people do not know how to get off the list suggests that accountability has not kept pace with deployment. Departments have adopted the technology quickly, but the infrastructure for correcting mistakes has not been built out with the same urgency.

Third, the burden of fixing the error falls on the person who did nothing wrong. The grandmother did not create this problem. She should not have to navigate a bureaucratic maze to solve it. That is a design failure, not just a database failure.

What Drivers in This Situation Should Know

If you suspect you have been flagged incorrectly in a law enforcement database, there are a few steps worth taking. Start by contacting the law enforcement agency that pulled you over and ask specifically which database generated the alert. Request written documentation of the alert and the plate information that triggered it. If the error involves a Flock Safety system, the local department that administers the camera contract is typically the right starting point, since Flock itself operates the technology on behalf of agencies.

From there, a formal written request to have the incorrect entry reviewed and corrected is more effective than a phone call. If the agency is unresponsive, contacting a local news outlet, as the Cherry Hills driver effectively did, has proven to actually move things along. An attorney who handles civil rights or consumer protection cases may also be able to advise on whether there are additional remedies available, particularly if repeated stops have caused documented harm.

The broader fix, however, needs to happen at the policy level. Automated license plate reader programs need clear error correction protocols, transparent processes for flagged individuals to dispute their status, and regular audits of the data that feeds these systems. Until that happens, the next person getting pulled over on their way to the grocery store might be anyone.