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Purportedly AI-Assisted Citation Errors Allegedly Found in Newfoundland and Labrador's 2025 Health Workforce Report by Deloitte

3 news reports Snapshot 7 Sep 2026

In brief

An AI system built by Openai and deployed by Deloitte allegedly harmed Healthcare Policymakers, Government Of Newfoundland And Labrador and 7 others.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
3 reportsMay 2025 - Nov 2025

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.

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.

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 (3)

Titles link to the original publisher; report text is not reproduced here.

  1. Provincial Government Releases Health Human Resources Plan
    gov.nl.ca · Government of Newfoundland and Labrador
  2. Major N.L. healthcare report contains errors likely generated by A.I.
    theindependent.ca · Justin Brake
  3. Deloitte allegedly cited AI-generated research in a million-dollar report for a Canadian provincial government
    fortune.com · Nino Paoli

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
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...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • 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...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • 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....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • 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...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Deloitte

Source record: incident #1286 on the AI Incident Database · all 3 reports