AI incident ·
London Metropolitan Police's Facial Recognition Technology Reportedly Misidentified Shaun Thompson as Suspect Leading to Arrest
In brief
An AI system built by Facial recognition system developers and deployed by Metropolitan Police Service, Law enforcement and 1 other allegedly harmed Shaun Thompson, General public of the United Kingdom and 1 other.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 3 reports
What happened
Sometime in February 2024, Shaun Thompson is reported to have walked by one of the London Metropolitan Police's facial recognition technology vans near London Bridge. He was almost immediately arrested because the technology is reported to have misidentified him as a suspect in an unrelated and unspecified crime.
Editor's notes
Incidents 691 and 692 are paired together in the reporting, but they are two separate, discrete harm events. I have created distinct incident IDs for each while replicating the reporting for each one. The incident date of 2/1/2024 gestures toward the fact that we seem only to have "sometime in February" as the date of Shaun Thompson's misidentification and arrest.
Laws that address this harm
Policy angle: Classified under Discrimination and Toxicity (Unequal performance across groups) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- Colorado AI Act
- India DPDP Act
- Law No. 132/2025 on artificial intelligence
- NYC Local Law 144 (automated employment decision tools)
- EU AI Act
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (3)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged developer
- Facial recognition system developers
- Alleged harmed party
- Shaun Thompson General public of the United Kingdom General public
AI systems implicated
Facial recognition systemsAI-enabled decision support systems
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.3 Unequal performance across groups
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 1.3.
- Bias and discrimination (value embedding)
"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models...
- Impact on affected communities
"It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the r...
- Unfair capability distribution
"Performing worse for some groups than others in a way that harms the worse-off group"
- Disparate Performance
The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups...
- Fairness
Avoiding bias and ensuring no disparate performance
- Ideological Homogenization from Value Embedding
"The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world c...
- Fairness
This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved o...
- Increased labor
increased burden (e.g., time spent) or effort required by members of certain social groups to make systems or products work as well for them as others
Related incidents
Linked by editors or by text similarity in the source dataset.
- Facewatch Reported to Have Wrongfully Flagged Home Bargains Customer as Shoplifter
- ETS Used Allegedly Flawed Voice Recognition Evidence to Accuse and Assess Scale of Cheating, Causing Thousands to be Deported from the UK
- Skating Rink’s Facial Recognition Cameras Misidentified Black Teenager as Banned Troublemaker
- Opaque Fraud Detection Algorithm by the UK’s Department of Work and Pensions Allegedly Discriminated against People with Disabilities
Incidents in the same risk subdomain
- Washington State DOL's AI Phone System Reportedly Failed to Provide Spanish-Language Service to Callers Requesting Spanish
- UK Facial Recognition System Reportedly Exhibits Higher False Positive Rates for Black and Asian Subjects
- Infinite Campus AI-Driven Student Risk Model Leads to Cuts in Support for Nevada's Low-Income Schools
- Police Use of Facial Recognition Software Causes Wrongful Arrests Without Defendant Knowledge
- Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud
- Facewatch Reported to Have Wrongfully Flagged Home Bargains Customer as Shoplifter
Other incidents involving Metropolitan Police Service
Source record: incident #692 on the AI Incident Database · all 3 reports