AI incident ·
Deepfake Obama Introduction of Deepfakes
On this page 8 sections
In brief
An AI system built and deployed by University Of Washington and Fakeapp allegedly harmed Barack Obama.
- Risk domain
- Misinformation
- Occurred
- Coverage
- 29 reports
What happened
University of Washington researchers made a deepfake of Obama, followed by Jordan Peele
Laws on record for this risk area
Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 instruments on record cover this risk area in the United States.
- Texas Responsible AI Governance Act (TRAIGA)
- California SB 53
- Tennessee ELVIS Act
- EO 14179
- New York RAISE Act (frontier model safety)
Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (29)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- University Of Washington, Fakeapp
- Alleged developer
- University Of Washington, Fakeapp
- Alleged harmed party
- Barack Obama
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Misinformation
- Risk subdomain
- 3.1 False or misleading information
- Causal entity
- AI
- Intent
- Intentional
- Timing
- Post-deployment
- Harm level
- none
- Sectors
- arts, entertainment and recreation, professional, scientific and technical activities
- Countries
- US
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 3.1.
- Pursuing Consistent Context
"LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...
- Defective Decoding Process
In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...
- Noisy Training Data
"Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....
- Untruthful Content
"The LLM-generated content could contain inaccurate information"
- Hallucinations
"LLMs generate nonsensical, untruthful, and factual incorrect content"
- Faithfulness Errors
"The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used
- Factuality Errors
"The LLM-generated content could contain inaccurate information" which is factually incorrect
- Knowledge Gaps
"Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...
Incidents in the same risk subdomain
- Nonfiction Book 'The Future of Truth' Reportedly Included AI-Generated and Misattributed Quotations
- Claude Console Reportedly Generated Phantom Legal Quotations in Trump Layoffs Court Filing
- Purportedly AI-Enhanced Images of Iranian Women Protesters Were Reportedly Spread With Unverified Execution Claims
- South Africa Draft National AI Policy Reportedly Included Fictitious References Believed to Be AI Hallucinations
- Purportedly AI-Generated Image Reportedly Misled Daejeon Authorities Searching for Escaped Wolf Neukgu
- Gemini and Grok Reportedly Misidentified Authentic Minab School-Strike Graveyard Photo as Unrelated Disaster Imagery
Source record: incident #39 on the AI Incident Database · all 29 reports