Skip to Content
logologo
AI Incident Database
Open TwitterOpen RSS FeedOpen FacebookOpen LinkedInOpen GitHub
Open Menu
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse

Incident 118: OpenAI's GPT-3 Associated Muslims with Violence

Description: Users and researchers revealed generative AI GPT-3 associating Muslims to violence in prompts, resulting in disturbingly racist and explicit outputs such as casting Muslim actor as a terrorist.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: OpenAI developed and deployed an AI system, which harmed Muslims.

Incident Stats

Incident ID
118
Report Count
3
Incident Date
2020-08-06
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

118

Special Interest Intangible Harm

An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
 

yes

Date of Incident Year

The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank. Enter in the format of YYYY
 

2021

Date of Incident Month

The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank. Enter in the format of MM
 

01

Date of Incident Day

The day on which the incident occurred. If a precise date is unavailable, leave blank. Enter in the format of DD
 

18

Estimated Date

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

Yes

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
 

1.1. Unfair discrimination and misrepresentation

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. Discrimination and Toxicity

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

Incident OccurrencePersistent Anti-Muslim Bias in Large Language ModelsGPT-3 is the world’s most powerful bigotry generator. What should we do about it?AI’s Islamophobia problem
Persistent Anti-Muslim Bias in Large Language Models

Persistent Anti-Muslim Bias in Large Language Models

arxiv.org

GPT-3 is the world’s most powerful bigotry generator. What should we do about it?

GPT-3 is the world’s most powerful bigotry generator. What should we do about it?

thenextweb.com

AI’s Islamophobia problem

AI’s Islamophobia problem

vox.com

Persistent Anti-Muslim Bias in Large Language Models
arxiv.org · 2021

It has been observed that large-scale language models capture undesirable societal biases, e.g. relating to race and gender; yet religious bias has been relatively unexplored. We demonstrate that GPT-3, a state-of-the-art contextual languag…

GPT-3 is the world’s most powerful bigotry generator. What should we do about it?
thenextweb.com · 2021

GPT-3 is, arguably, the world’s most advanced text generator. It costs billions of dollars to develop, has a massive carbon footprint, and was trained by some of the world’s leading AI experts using one of the largest datasets ever curated.…

AI’s Islamophobia problem
vox.com · 2021

Imagine that you’re asked to finish this sentence: “Two Muslims walked into a …”

Which word would you add? “Bar,” maybe?

It sounds like the start of a joke. But when Stanford researchers fed the unfinished sentence into GPT-3, an artificial…

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.

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Research Prototype AI, Delphi, Reportedly Gave Racially Biased Answers on Ethics

Research Prototype AI, Delphi, Reportedly Gave Racially Biased Answers on Ethics

Oct 2021 · 3 reports
Tougher Turing Test Exposes Chatbots’ Stupidity (migrated to Issue)

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

Jul 2016 · 1 report
WeChat’s Machine Translation Gave a Racist English Translation for the Chinese Term for “Black Foreigner”

WeChat’s Machine Translation Gave a Racist English Translation for the Chinese Term for “Black Foreigner”

Oct 2017 · 5 reports
Previous IncidentNext Incident

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Research Prototype AI, Delphi, Reportedly Gave Racially Biased Answers on Ethics

Research Prototype AI, Delphi, Reportedly Gave Racially Biased Answers on Ethics

Oct 2021 · 3 reports
Tougher Turing Test Exposes Chatbots’ Stupidity (migrated to Issue)

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

Jul 2016 · 1 report
WeChat’s Machine Translation Gave a Racist English Translation for the Chinese Term for “Black Foreigner”

WeChat’s Machine Translation Gave a Racist English Translation for the Chinese Term for “Black Foreigner”

Oct 2017 · 5 reports

Research

  • Defining an “AI Incident”
  • Defining an “AI Incident Response”
  • Database Roadmap
  • Related Work
  • Download Complete Database

Project and Community

  • About
  • Contact and Follow
  • Apps and Summaries
  • Editor’s Guide

Incidents

  • All Incidents in List Form
  • Flagged Incidents
  • Submission Queue
  • Classifications View
  • Taxonomies

2023 - AI Incident Database

  • Terms of use
  • Privacy Policy
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • 9d70fba