AI incident ·
Cody Enterprise Reporter Resigns After Admitting to AI-Generated Fake Quotes
In brief
An AI system built by Chatbots and deployed by Aaron Pelczar allegedly harmed Mark Gordon, Wyoming Officials and 3 others.
- Risk domain
- Misinformation
- Occurred
- Coverage
- 4 reports
What happened
A reporter at the Cody Enterprise used AI to generate fake quotes and stories, including fabricating statements from Wyoming’s governor and other officials. The misuse of AI was uncovered when another journalist noticed robotic phrases and false information in the articles. The reporter resigned, and the newspaper is now implementing policies to prevent future incidents.
Laws that address this harm
Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (4)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Aaron Pelczar
- Alleged developer
- Chatbots
- Alleged harmed party
- Mark Gordon, Wyoming Officials, Cody Enterprise, Cody Enterprise Readers, Journalistic Integrity
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Misinformation
- Risk subdomain
- 3.1 False or misleading information
- Causal entity
- Human
- Intent
- Intentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
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....
- 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
- Untruthful Content
"The LLM-generated content could contain inaccurate information"
- 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 #764 on the AI Incident Database · all 4 reports