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Incident 316: Facebook Ad-Approval Algorithm Allegedly Missed Fraudulent Ads via Simple URL Checks

Description: Facebook’s advertisement-approval algorithm was reported by a security analyst to have neglected simple checks for domain URLs, leaving its users at risk of fraudulent ads.

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Alleged: Facebook developed and deployed an AI system, which harmed Facebook users.

Incident Stats

Incident ID
316
Report Count
1
Incident Date
2016-06-02
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
 

4.3. Fraud, scams, and targeted manipulation

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. Malicious Actors & Misuse

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

+1
Bait and Switch: The Failure of Facebook Advertising — An OSINT Investigation
Bait and Switch: The Failure of Facebook Advertising — An OSINT Investigation

Bait and Switch: The Failure of Facebook Advertising — An OSINT Investigation

medium.com

Bait and Switch: The Failure of Facebook Advertising — An OSINT Investigation
medium.com · 2016

This story starts with Sidney Crosby. A professional hockey player and Canadian icon. I can neither confirm nor deny that I welled up like a small child at the Hockey Hall of Fame’s “Golden Goal” display (I will never forget that moment). S…

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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