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Incident 29: Image Classification of Battle Tanks

Description: A potentially apocryphal story in which an image classifier was produced to differentiate types of battle tanks, but the resulting model keyed in on environmental attributes rather than tank attributes

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Entities

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Alleged: United States Government developed and deployed an AI system, which harmed United States Government.

Incident Stats

Incident ID
29
Report Count
3
Incident Date
2011-09-20
Editors
Sean McGregor
Applied Taxonomies
CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

29

Estimated Date

“Yes” if the data was estimated. “No” otherwise.
 

No

Lives Lost

Indicates the number of deaths reported
 

0

Injuries

Indicate the number of injuries reported.
 

0

Estimated Harm Quantities

Indicates if the amount was estimated.
 

No

There is a potentially identifiable specific entity that experienced the harm

A potentially identifiable specific entity that experienced the harm can be characterized or identified.
 

No

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
 

Pre-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
The Neural Net Tank Urban Legend
Tales from the Trenches: AI Disaster Stories (GDC talk)AI Incident Database Incidents Converted to Issues
The Neural Net Tank Urban Legend

The Neural Net Tank Urban Legend

gwern.net

Tales from the Trenches: AI Disaster Stories (GDC talk)

Tales from the Trenches: AI Disaster Stories (GDC talk)

lobste.rs

AI Incident Database Incidents Converted to Issues

AI Incident Database Incidents Converted to Issues

github.com

The Neural Net Tank Urban Legend
gwern.net · 2011

Drawing on Google/Google Books/Google Scholar/Libgen/LessWrong/Hacker News/Twitter, I have compiled a large number of variants of the story from various sources; below, in reverse chronological order by decade.

A similar thing happened here…

Tales from the Trenches: AI Disaster Stories (GDC talk)
lobste.rs · 2016

His team was working on running simulations of long-distance manned spaceflight. In particular, the goal of their simulations was to determine an algorithm that would optimally allocate food, water, and electricity to 3 crew members. The de…

AI Incident Database Incidents Converted to Issues
github.com · 2022

The following former incidents have been converted to "issues" following an update to the incident definition and ingestion criteria.

21: Tougher Turing Test Exposes Chatbots’ Stupidity

Description: The 2016 Winograd Schema Challenge highli…

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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