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Incident 233: Tumblr Automated Pornography-Detecting Algorithms Erroneously Flagged Inoffensive Images as Explicit

Description: Tumblr’s automated tools to identify adult content were reported to have incorrectly flagged inoffensive images as explicit, following its announcement to ban all adult content on the platform.

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Alleged: Tumblr developed and deployed an AI system, which harmed Tumblr content creators and Tumblr users.

Incident Stats

Incident ID
233
Report Count
1
Incident Date
2018-12-03
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
 

7.3. Lack of capability or robustness

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. AI system safety, failures, and limitations

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

+1
Tumblr is already flagging innocent posts as porn
Tumblr is already flagging innocent posts as porn

Tumblr is already flagging innocent posts as porn

theverge.com

Tumblr is already flagging innocent posts as porn
theverge.com · 2018

Tumblr announced earlier today that it will ban all adult content on the platform, starting on December 17th. Now, longtime users are criticizing the company’s auto-detecting algorithms, which appear to be incorrectly flagging some inoffens…

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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