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Incident 21: Tougher Turing Test Exposes Chatbots’ Stupidity (migrated to Issue)

Description: The 2016 Winograd Schema Challenge highlighted how even the most successful AI systems entered into the Challenge were only successful 3% more often than random chance. This incident has been downgraded to an issue as it does not meet current ingestion criteria.

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Entities

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

Incident Stats

Incident ID
21
Report Count
1
Incident Date
2016-07-14
Editors
Sean McGregor
Applied Taxonomies
CSETv0, GMF, CSETv1, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

21

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

GMF Taxonomy Classifications

Taxonomy Details

Known AI Goal Snippets

One or more snippets that justify the classification.
 

(Snippet Text: The Winograd Schema Challenge asks computers to make sense of sentences that are ambiguous but usually simple for humans to parse., Related Classifications: Question Answering)

CSETv0 Taxonomy Classifications

Taxonomy Details

Physical System

Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
 

Software only

Level of Autonomy

The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
 

High

Nature of End User

"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
 

Expert

Public Sector Deployment

"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
 

No

Lives Lost

Were human lives lost as a result of the incident?
 

No

Intent

Was the incident an accident, intentional, or is the intent unclear?
 

Unclear

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

Incident OccurrenceAI Incident Database Incidents Converted to Issues
AI Incident Database Incidents Converted to Issues

AI Incident Database Incidents Converted to Issues

github.com

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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