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Incident 411: Chinese Accounts Spammed Twitter Feed Allegedly to Obscure News of Protests

Description: Twitter Feed was flooded by content from Chinese-language accounts which allegedly aimed to manipulate and reduce social media coverage about widespread protests against coronavirus restrictions in China.

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Alleged: Twitter developed and deployed an AI system, which harmed Twitter and Twitter Users.

Incident Stats

Incident ID
411
Report Count
1
Incident Date
2022-11-27
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
 

4.1. Disinformation, surveillance, and influence at scale

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
Twitter grapples with Chinese spam obscuring news of protests
Twitter grapples with Chinese spam obscuring news of protests

Twitter grapples with Chinese spam obscuring news of protests

washingtonpost.com

Twitter grapples with Chinese spam obscuring news of protests
washingtonpost.com · 2022

SAN FRANCISCO — Twitter's radically reduced anti-propaganda team grappled on Sunday with a flood of nuisance content in China that researchers said was aimed at reducing the flow of news about stunning widespread protests against coronaviru…

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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