Kansas Couple Reportedly Stopped Twice in Kansas and Oklahoma After Flock Safety Alerts Linked Their Plate Number to a Stolen Vehicle

Matthew and Leah Spurlock were reportedly stopped in Colwich, Kansas, and about a month later in Catoosa, Oklahoma, after Flock Safety cameras associated their KU alumni plate 7007 with a stolen GMC pickup bearing a temporary Kansas tag with the same number. Colwich police quickly resolved the first stop; in Catoosa, the couple said officers drew guns and handcuffed Matthew. Wichita police said Flock reads plate characters but not plate type.

Risk classification

• Primary risk domain: 7 AI system safety, failures, & limitations
• Primary risk subdomain: 7.3 Lack of capability or robustness

The Flock camera system failed to perform robustly because it lacks the capability to distinguish between different license plate backgrounds with identical alphanumeric characters, leading to false stolen-vehicle alerts.

Additional risk subdomains

• 5.1 Overreliance and unsafe use: Police officers in Catoosa overrelied on the automated Flock camera alert to conduct a high-risk, armed stop without first performing the required manual verification of the vehicle make and model.

Causal factors

• Entity: AI
• Intent: Unintentional
• Timing: Post-deployment

The incident was caused by the Flock AI camera system failing to distinguish license plate backgrounds post-deployment, leading to an unintended high-risk police stop.

EU AI Act risk tier

• Risk tier: 2 High Risk

High Risk: The system is used in law enforcement applications (automatic license plate recognition for identifying stolen vehicles), which has significant implications for public safety and fundamental civil rights.

AI system and alleged parties

• AI system: Flock Safety cameras (Flock Safety)
• AI purpose: License Plate Recognition; Threat Detection
• Behaviour type: Tool
• Alleged developer: Surveillance technology, Privacy, Flock Safety, Epistemic integrity, Automated license plate reader (ALPR) developers
• Alleged deployer: Police departments in the United States, Law enforcement, Colwich Police Department (Kansas), Catoosa Police Department (Oklahoma)
• Alleged harmed parties: Road users, Motorists, Matthew Spurlock, Leah Spurlock, Drivers

Harm severity

Severity ratings are based on this scale.

Highest direct severity in any category: Minor. Severity is scored from Negligible to Catastrophic in each harm category, for harm the reports describe as caused directly or indirectly by the AI system.

• Physical: direct Negligible, indirect Negligible
• Infrastructure: direct Negligible, indirect Negligible
• Property: direct Negligible, indirect Negligible
• Financial: direct Negligible, indirect Negligible
• Environmental: direct Negligible, indirect Negligible
• Malicious content: direct Negligible, indirect Negligible
• Differential treatment: direct Negligible, indirect Negligible
• Civil rights: direct Negligible, indirect Negligible
• Democracy: direct Negligible, indirect Negligible
• Privacy: direct Negligible, indirect Negligible
• Psychological: direct Minor, indirect Negligible
• Epistemic: direct Negligible, indirect Negligible
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Psychological

Reported: The report explicitly describes psychological distress and fear during the high-risk police stop.

Directly caused: Matthew Spurlock reported being scared and experiencing a highly stressful situation while officers pointed drawn guns at him and gave verbal commands over a PA system.

Inferred additional harm: Both Matthew and Leah Spurlock likely experienced temporary acute anxiety and trauma from being held at gunpoint by multiple police officers.

People affected

• Occurrences reported: 2
• People reportedly harmed: 2
• People reportedly exposed: 2

Potential causes

Management

• Premature System Deployment: Deploying ALPR technology in areas with duplicate plates without safeguards.
• Lack of Operator Training: Insufficient training on how to handle and verify automated plate reader alerts.

Technology

• No Plate Background Recognition: AI system only reads alphanumeric characters, ignoring specialty plate designs.
• Inability to Distinguish Plate Types: Flock cameras cannot differentiate between standard, KU, or veteran tags.

Data Inputs

• Duplicate License Plate Numbers: Kansas state issues identical alphanumeric combinations across different plate types.
• Incorrect Plate Type Entry: Human operators entered the incorrect plate design category into the database.

Human Factors

• Overreliance on Camera Alerts: Officers acted aggressively on automated alerts without manual visual verification.
• Failure to Verify Vehicle Match: Officers did not verify if the vehicle type matched the stolen vehicle record.

Process and Methods

• Inadequate Alert Verification: Lack of robust secondary validation protocol before initiating high-risk stops.
• Systemic Database Entry Errors: The process allows incorrect vehicle or plate metadata to be saved as active.

Regulatory Environment

• Permissive Plate Duplication Policy: State regulations allowed duplicate numbers across dozens of specialty plates.

Information quality

• Classification confidence: High
• Reason for confidence: The report provides clear, first-hand accounts from the victims, statements from local police departments, and technical explanations of how the Flock cameras operate and why the error occurred.

An automated license plate reader failed to distinguish between unique state plate backgrounds with identical alphanumeric characters, leading police to conduct a high-risk traffic stop on innocent citizens. This incident highlights domestic civil liberties and local law enforcement verification challenges rather than a direct threat to national security.

• Overall national security impact: Minor
• Scope: Single nation
• Primary target: United States
• Alleged perpetrator: Unknown

Threat characteristics

• Imminence: Long-term. This represents an ongoing system limitation and policy challenge rather than an active, imminent national security crisis.
• Autonomy: Human-supervised. The AI system flagged the license plate autonomously, but human law enforcement officers made the final decision to conduct the stop and detain the individuals.
• Novelty: Established threat. False positives in automated license plate readers and database mismatches are known issues that have occurred in law enforcement previously.

Impact by dimension

• Physical security: Minor. While police drew weapons during a high-risk traffic stop, creating physical danger, it was a localized law enforcement error rather than a threat to critical infrastructure or national physical security.
• Information security: Negligible. No intelligence compromise, classified data theft, or systematic information warfare operations occurred.
• Sovereignty: Minor. Local law enforcement operations were disrupted by false alerts, but there was no threat to state authority, constitutional processes, or core federal government functions.
• Economic security: Negligible. No significant threat to strategic industries, financial systems, or national technological competitive advantage.
• Societal stability: Minor. The incident involved a violation of civil liberties and arbitrary detention of citizens due to an automated system failure, but it does not represent mass societal destabilization.