AI incident #1286 ·
Purportedly AI-Assisted Citation Errors Allegedly Found in Newfoundland and Labrador's 2025 Health Workforce Report by Deloitte
What happened
A Deloitte-authored Health Human Resources Plan released by Newfoundland and Labrador in May 2025 was later reported by The Independent to contain several inaccurate or apparently non-existent research citations. Researchers named in the disputed citations reportedly denied authoring the referenced papers. Deloitte stated that AI was selectively used to support some citation work and that it is revising the report.
Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.
News reports (3)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Deloitte
- Alleged developer
- Openai
- Alleged harmed party
- Healthcare Policymakers, Government Of Newfoundland And Labrador, General Public Of Newfoundland And Labrador, General Public Of Canada, General Public, Epistemic Integrity, Martha Macleod, Gail Tomblin Murphy, Canadian Journal Of Respiratory Therapy
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Misinformation
- Risk subdomain
- 3.1 False or misleading information
- Causal entity
- AI
- Intent
- Unintentional
- 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...
- 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...
- 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
- 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...
- Untruthful Content
"The LLM-generated content could contain inaccurate information"
- Factuality Errors
"The LLM-generated content could contain inaccurate information" which is factually incorrect
- 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....
Incidents in the same risk subdomain
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- Claude Console Reportedly Generated Phantom Legal Quotations in Trump Layoffs Court Filing
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- 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