AI incident ·
UK Facial Recognition System Reportedly Exhibits Higher False Positive Rates for Black and Asian Subjects
In brief
An AI system built by Unknown Facial Recognition Technology Developers and deployed by Home Office, Metropolitan Police and 3 others allegedly harmed General Public, General Public Of The United Kingdom and 5 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 4 reports
What happened
UK government testing of police facial recognition technology reportedly found significantly higher false positive identification rates for Black and Asian individuals compared with white subjects, with particularly elevated error rates for Black women. The findings reportedly emerged from analysis of retrospective searches of the police national database and were disclosed by the Home Office amid plans for expanded national deployment.
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 (4)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Home Office, Metropolitan Police, Government Of The United Kingdom, Law Enforcement, British Law Enforcement
- Alleged developer
- Unknown Facial Recognition Technology Developers
- Alleged harmed party
- General Public, General Public Of The United Kingdom, Minorities In The United Kingdom, Black People In The United Kingdom, Asian People In The United Kingdom, Epistemic Integrity, National Security And Intelligence Stakeholders
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
Incidents in the same risk subdomain
- Washington State DOL's AI Phone System Reportedly Failed to Provide Spanish-Language Service to Callers Requesting Spanish
- 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
- Whisper Speech-to-Text AI Reportedly Found to Create Violent Hallucinations
Source record: incident #1305 on the AI Incident Database · all 4 reports