A traffic enforcement system in Bath, UK, incorrectly issued a fine to a vehicle owner after an automated license plate recognition camera misidentified text on a pedestrian's shirt as a license plate. The error was discovered when the vehicle owners contacted local officials, who subsequently cancelled the fine.
A Bath resident was wrongly fined by the local officials because an automated license plate recognition camera misread the text on her shirt as a license plate number.
Risk classification
- Primary risk domain: 7 AI system safety, failures, & limitations
- Primary risk subdomain: 7.3 Lack of capability or robustness
The AI system failed to perform robustly under real-world conditions, misidentifying text on a pedestrian's t-shirt as a vehicle license plate.
Causal factors
- Entity: AI
- Intent: Unintentional
- Timing: Post-deployment
The incident was caused by an automated license plate recognition AI system making an unexpected classification error after being deployed.
EU AI Act risk tier
High Risk: The system is used in law enforcement and traffic management, which are classified as high-risk applications under the EU AI Act.
AI system and alleged parties
- AI system: automated license plate recognition camera
- AI purpose: License Plate Recognition; Identification
- Behaviour type: Autonomous
- Alleged developer: unknown
- Alleged deployer: Bath government
- Alleged harmed parties: UK public, Paula Knight, Bath officials
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 Negligible
- Epistemic: direct Negligible, indirect Negligible
- Child sexual exploitation and abuse: direct Negligible, indirect Negligible
People affected
- Occurrences reported: 1
- People reportedly harmed: 2
- People reportedly exposed: 2
Potential causes
Management
- Prioritizing Cost over Accuracy: Automation was deployed to reduce labor costs without ensuring reliability.
Technology
- OCR Misidentification: The camera read t-shirt text 'KNITTER' as a vehicle license plate.
- Flawed Object Classification: The system failed to distinguish a pedestrian from a motor vehicle.
Data Inputs
- Ambiguous Text Input: The t-shirt text closely resembled the personalized plate 'KN19 TER'.
Human Factors
- Lack of Human Oversight: No staff reviewed the computer-generated photo before sending the fine.
Process and Methods
- Fully Automated Ticket Issuance: The process generated and mailed citations without any manual validation.
Information quality
- Classification confidence: High
- Reason for confidence: The report clearly details the sequence of events, the specific error made by the AI system, and the resolution of the incident. There is no conflicting information or significant ambiguity regarding the system's failure.
An automated traffic enforcement camera in Bath, UK, misidentified text on a pedestrian's shirt as a license plate, resulting in an erroneous fine. The incident has negligible national security implications, representing a minor and easily corrected administrative error with no hostile intent or systemic impact.
- Overall national security impact: Negligible
- Scope: Single nation
- Primary target: United Kingdom
- Alleged perpetrator: Unknown
Threat characteristics
- Imminence: Long-term. The incident is a resolved administrative error with no active or imminent national security threat.
- Autonomy: Full autonomy. The automated system processed the image, matched it to the database, and issued the fine without any human oversight or intervention.
- Novelty: Established threat. False positives and character recognition errors in automated license plate readers are well-documented and common occurrences.
Impact by dimension
- Physical security: Negligible. The incident involves a local traffic enforcement error with no impact on physical systems, critical infrastructure, or human safety.
- Information security: Negligible. No intelligence capabilities, classified information, or information security systems were compromised or targeted.
- Sovereignty: Negligible. While a local government automated system made an error, it did not threaten state authority, territorial control, or core national government operations.
- Economic security: Negligible. The financial impact was limited to a single disputed traffic fine of 124 USD which was cancelled, presenting no threat to economic or technological security.
- Societal stability: Negligible. The incident represents an isolated automated billing error affecting a single couple, with no systemic threat to civil liberties or societal stability.
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