Flock Safety License-Plate Reader Reportedly Misread Plate Digit, Prompting Gunpoint Stop of Two Sisters in Española, New Mexico

In July 2023, Española police stopped Jaclynn Gonzales and her 12-year-old sister after a Flock Safety license-plate reader reportedly read the final digit of Gonzales's plate as 7 rather than 2, matching a stolen plate. Body-camera reporting showed officers drew guns and handcuffed both before confirming the mismatch. The sisters later sued; Española police said officers followed procedure.

Risk classification

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

The primary failure was the AI system's lack of capability or robustness in accurately reading the license plate digit under real-world conditions, misidentifying a '2' as a '7'. Boundary rule B2 applies because the system performed below reasonable expectations (a direct reading error), making the system the locus of failure.

Additional risk subdomains

• 5.1 Overreliance and unsafe use: Police officers overrelied on the automated camera alert, immediately conducting high-risk, armed traffic stops and handcuffing minors without independent verification of the vehicle details.

Causal factors

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

The incident was caused by a deployed Flock Safety license plate recognition camera system misreading a digit, which was an unexpected and unintentional failure of the system's computer vision capabilities.

EU AI Act risk tier

• Risk tier: 2 High Risk

High Risk: The AI system is used in a law enforcement application (license plate recognition and traffic surveillance), which has significant implications for public safety and fundamental civil rights.

AI system and alleged parties

• AI system: Flock Safety license-plate reader (Flock Safety)
• AI purpose: License Plate Recognition; Threat Detection
• Behaviour type: Tool
• Alleged developer: Surveillance technology developers, Flock Safety, Automated license plate reader (ALPR) developers
• Alleged deployer: Law enforcement, Española Police Department (New Mexico)
• Alleged harmed parties: Women and girls, Women, Road users, Privacy, Motorists, Misidentified individuals, Minors, Jaclynn Gonzales and her 12-year-old sister, Girls, Epistemic integrity

Harm severity

Severity ratings are based on this scale.

Highest direct severity in any category: Negligible. 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 Negligible, indirect Minor
• Epistemic: direct Negligible, indirect Negligible
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Psychological

Reported: The report explicitly describes psychological harm, noting the sisters suffered personal injuries including anxiety, fear, and worry.

Indirectly caused: Three individuals (the 21-year-old driver, her 12-year-old sister, and a 17-year-old student in a separate incident) experienced severe fear, anxiety, and trauma from being held at gunpoint or handcuffed by police due to the faulty alerts.

Inferred additional harm: It is highly likely that the family members of the victims, particularly the mother of the 12-year-old, experienced significant distress and anxiety due to the incident.

People affected

• Occurrences reported: 2
• People reportedly harmed: 3
• People reportedly exposed: 3

Potential causes

Management

• Inadequate Officer Training: Police management failed to train officers on the limitations of ALPR tech.
• Lack of System Oversight: Management did not monitor or audit camera errors and subsequent bad stops.

Technology

• OCR Character Misclassification: The Flock Safety camera incorrectly read the license plate digit 2 as a 7.
• Algorithmic Flaw in Plate Matching: The automated system flagged a vehicle with a different plate digit as stolen.

Data Inputs

• Incorrect License Plate Reading: The system captured and processed incorrect alphanumeric data from the plate.
• Inaccurate Alert Database Entry: An officer entered faulty vehicle details into the alert system manually.

Human Factors

• Blind Reliance on Camera Alerts: Officers acted aggressively on the automated alert without verifying the plate.
• Failure to Verify Vehicle Details: Officer ignored differences between the suspect vehicle and the stopped vehicle.
• Officer Misinterpretation of Video: An officer used video of a non-matching truck to create a police-wide alert.

Process and Methods

• Lack of Manual Plate Verification: No process required officers to double-check the physical plate before a stop.
• Flawed Alert Dissemination Process: System allowed faulty alert details to be broadcasted to other agencies.
• Over-aggressive Stop Protocols: Standard operating procedures led to immediate handcuffing of minors.

Regulatory Environment

• Lack of ALPR Operational Standards: No regulatory standards governing how police verify automated camera alerts.

Information quality

• Classification confidence: High
• Reason for confidence: The report provides clear, factual details of two specific incidents in Espanola, New Mexico, naming the victims, the law enforcement agencies, the specific technology vendor (Flock Safety), and the exact nature of the system error (reading a 2 as a 7).

A commercial license plate recognition system misidentified a digit, leading local police to mistakenly detain innocent civilians at gunpoint. While raising significant civil liberties and local public safety concerns, the incident has negligible national security implications.

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

Threat characteristics

• Imminence: Long-term. The incident represents an ongoing, localized technology integration issue rather than an active national security crisis requiring urgent federal response.
• Autonomy: Human-supervised. The AI system flagged the vehicle autonomously, but human police officers made the final decision to execute the traffic stop and arrest the individuals.
• Novelty: Established threat. Errors in automated license plate readers and subsequent mistaken police stops are a known, recurring issue with established precedents.

Impact by dimension

• Physical security: Minor. The incident led to high-stress, armed police stops where citizens were held at gunpoint and handcuffed, posing a direct threat to human physical safety, though it did not involve critical infrastructure compromise or mass casualties.
• Information security: Negligible. No indication of intelligence compromise, classified data theft, or systematic adversarial information warfare operations.
• Sovereignty: Minor. Local law enforcement decision-making was compromised by relying on faulty automated alerts, leading to wrongful detentions, which minimally impacts local government function.
• Economic security: Negligible. No threat to strategic industries, critical supply chains, or national technological competitive advantage.
• Societal stability: Minor. The incident involved violations of civil liberties, unlawful detention, and psychological trauma of innocent individuals due to automated surveillance errors, but it remains a localized policing issue rather than systematic societal oppression.