On Flock License Plate Tracking Cameras

2026-07-20 · Source: Schneier on Security · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Novice, short

Summary

A recent incident highlighted critical flaws in Flock license plate tracking cameras, where a writer was mistakenly identified, tracked, and arrested. The system flagged a partial plate, "34 DTM," matching a stolen vehicle (34 03 DTM) to the writer's car (34 10 DTM), leading to a nationwide issue for Jaguar Land Rover's "34 ## DTM" media fleet. Flock Safety affirmed its machine learning system functioned as designed, stating it flags partial matches per law enforcement requests, and placed responsibility on officers to verify full plate details. Separately, Flock's CEO apologized for previously labeling privacy advocates as "terrorists." Concerns also persist regarding police using the network to track individuals based on descriptions like "heavy-set male" rather than just vehicles, raising abuse potential.

Key takeaway

For policy makers evaluating surveillance technology, understand that systems like Flock's, designed for partial matches, can generate widespread false positives and enable tracking beyond vehicles. You must mandate strict verification protocols for law enforcement using such tools. Ensure clear guidelines prevent misuse for tracking individuals based on vague descriptions, and establish robust audit mechanisms to prevent abuses and protect civil liberties.

Key insights

License plate recognition systems, designed for partial matches, can lead to widespread mistaken identity and privacy concerns.

Principles

• Partial plate matching risks widespread false positives.
• ML systems prioritize law enforcement search criteria.
• Surveillance tech often expands beyond vehicle tracking.

In practice

• Verify full plate details before acting on alerts.
• Correct initial police reports to update tracking systems.
• Audit surveillance system search parameters.

Topics

• License Plate Recognition
• Flock Safety Cameras
• Machine Learning Accuracy
• Police Surveillance
• Privacy Rights
• False Positives

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Ethicist, Policy Maker, Legal Professional

Related on AIssential

• Flock Cameras Can Surveil Cars Without License Plates — Surveillance tech like Flock's "Vehicle Fingerprint" enables persistent tracking using non-plate identifiers, raising significant privacy and legal concerns.
• LAPD Regularly Pulled Over Innocent People Because License Plate Readers Flagged Their Cars As Stolen — Automated license plate readers (ALPRs) can lead to widespread false positives and privacy infringements due to data inaccuracies.
• Cops Keep Getting Arrested for Using Flock to Stalk People — Police widely misuse Flock ALPR systems for personal stalking, enabled by insufficient oversight and lack of warrant requirements.
• Met police to pilot facial recognition identity checks, mayor confirms — Metropolitan Police are piloting mobile facial recognition for identity checks, sparking debate over civil liberties and regulation.
• More License Plate Reader Mission Creep: School Residency Verification, Background Checks, and Noise Complaints — The absence of warrant requirements for ALPR data has enabled widespread mission creep in law enforcement surveillance.
• Victory! Flock Ends Rollout of Audio “Distress Detection” of Human Voices — Public pressure can successfully halt the deployment of invasive surveillance technologies.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Schneier on Security.