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Incident 406: Facebook's Friend Suggestion Feature Recommends Patients of Psychiatrist to Each Other

Description: Facebook's "People You May Know" (PYMK) feature was reported by a psychiatrist for recommending her patients as friends through recommendations, violating patients' privacy and confidentiality.

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Alleged: Facebook developed and deployed an AI system, which harmed pseudonymized psychiatrist's patients , pseudonymized psychiatrist , patients and healthcare providers.

Incident Stats

Incident ID
406
Report Count
1
Incident Date
2015-07-15
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
 

2.1. Compromise of privacy by obtaining, leaking or correctly inferring sensitive information

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. Privacy & Security

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

Incident OccurrenceFacebook recommended that this psychiatrist's patients friend each other
Facebook recommended that this psychiatrist's patients friend each other

Facebook recommended that this psychiatrist's patients friend each other

splinternews.com

Facebook recommended that this psychiatrist's patients friend each other
splinternews.com · 2016

Facebook's ability to figure out the "people we might know" is sometimes eerie. Many a Facebook user has been creeped out when a one-time Tinder date or an ex-boss from 10 years ago suddenly pops up as a friend recommendation. How does the …

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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