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Incident 522: Facebook Political Ad Delivery Algorithms Inferred Users' Political Alignment, Inhibiting Political Campaigns' Reach

Description: Facebook's political ad delivery system reportedly differentiated the price of user reach based on their inferred political alignment, inhibiting political campaigns' ability to reach voters with diverse political views, which allegedly reinforces political polarization and creates informational filter bubbles.

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

Incident Stats

Incident ID
522
Report Count
1
Incident Date
2019-07-10
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
 

3.2. Pollution of information ecosystem and loss of consensus reality

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. Misinformation

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

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Incident OccurrenceAd Delivery Algorithms: The Hidden Arbiters of Political Messaging
Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging

Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging

arxiv.org

Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging
arxiv.org · 2019

Political campaigns are increasingly turning to digital advertising to reach voters. It is predicted that, during the 2020 U.S. presidential elections, 28% of political marketing spending will go to online advertising, compared to 20% in 20…

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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