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Incident 309: Facial Recognition Trial Performed Poorly at Notting Hill Carnival

Description: The facial recognition trial by London’s Metropolitan Police Service at the Notting Hill Carnival reportedly performed poorly with a high rate of false positives.

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Alleged: unknown developed an AI system deployed by Metropolitan Police Service, which harmed Notting Hill Carnival goers.

Incident Stats

Incident ID
309
Report Count
4
Incident Date
2017-08-26
Editors
Khoa Lam
Applied Taxonomies
MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

+1
Metropolitan Police’s use of Facial Recognition Technology at the Notting Hill Carnival, 2017
Police facial recognition trial led to 'erroneous arrest'Zero arrests, 2 correct matches, no criminals: London cops' facial recog tech slammedDon’t Believe the Algorithm
Metropolitan Police’s use of Facial Recognition Technology at the Notting Hill Carnival, 2017

Metropolitan Police’s use of Facial Recognition Technology at the Notting Hill Carnival, 2017

gov.uk

Police facial recognition trial led to 'erroneous arrest'

Police facial recognition trial led to 'erroneous arrest'

news.sky.com

Zero arrests, 2 correct matches, no criminals: London cops' facial recog tech slammed

Zero arrests, 2 correct matches, no criminals: London cops' facial recog tech slammed

theregister.com

Don’t Believe the Algorithm

Don’t Believe the Algorithm

wsj.com

Metropolitan Police’s use of Facial Recognition Technology at the Notting Hill Carnival, 2017
gov.uk · 2017

This kind of biometric technology (Facial Recognition Technology) has the potential to be a really useful crime fighting tool but we are not there yet. It needs to be properly tested and evaluated if it is going to be effective and it will …

Police facial recognition trial led to 'erroneous arrest'
news.sky.com · 2017

The controversial trial of facial recognition equipment at Notting Hill Carnival resulted in roughly 35 false matches and an 'erroneous arrest', highlighting questions about police use of the technology.

The system only produced a single ac…

Zero arrests, 2 correct matches, no criminals: London cops' facial recog tech slammed
theregister.com · 2018

London cops' facial recognition kit has only correctly identified two people to date – neither of whom were criminals – and the UK capital's police force has made no arrests using it, figures published today revealed.

According to informati…

Don’t Believe the Algorithm
wsj.com · 2018

The Notting Hill Carnival is Europe’s largest street party. A celebration of black British culture, it attracts up to two million revelers, and thousands of police. At last year’s event, the Metropolitan Police Service of London deployed a …

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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