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Incident 235: Chinese Insurer Ping An Employed Facial Recognition to Determine Customers’ Untrustworthiness, Which Critics Alleged to Likely Make Errors and Discriminate

Description: Customers’ untrustworthiness and unprofitability were reportedly determined by Ping An, a large insurance company in China, via facial-recognition measurements of micro-expressions and body-mass indices (BMI), which critics argue was likely to make mistakes, discriminate against certain ethnic groups, and undermine its own industry.

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Alleged: Ping An developed and deployed an AI system, which harmed Ping An customers and Chinese minority groups.

Incident Stats

Incident ID
235
Report Count
1
Incident Date
2016-04-15
Editors
Khoa Lam
Applied Taxonomies
GMF, 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
 

1.1. Unfair discrimination and misrepresentation

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. Discrimination and Toxicity

Entity

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

Human

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
 

Intentional

Incident Reports

Reports Timeline

Incident OccurrenceChina Knows How to Take Away Your Health Insurance
China Knows How to Take Away Your Health Insurance

China Knows How to Take Away Your Health Insurance

bloomberg.com

China Knows How to Take Away Your Health Insurance
bloomberg.com · 2019

China’s largest insurer, Ping An, has apparently started employing artificial intelligence to identify untrustworthy and unprofitable customers. It offers a chilling example of what, if we’re not careful, the future could look like here in …

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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