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AI incident #1084 ·

Federal 'Make America Healthy Again' Report Released with Multiple Reportedly Erroneous and Unverifiable Citations

Open on the AI Incident Database 3 news reports Snapshot 7 Sep 2026

What happened

The federal "Make America Healthy Again" (MAHA) report, released under HHS Secretary Robert F. Kennedy Jr., included hundreds of citations, some of which were reportedly nonexistent or erroneous. Analysts reportedly identified markers consistent with AI-generated text, such as repeated entries, nonexistent studies, and URLs containing "oaicite," suggesting use of tools like ChatGPT.

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.

  1. White House Health Report Included Fake Citations
    nytimes.com · Dani Blum, Maggie Astor · AIID #5273
  2. The MAHA Report’s AI fingerprints, annotated
    washingtonpost.com · Caitlin Gilbert, Emily Wright, Fenit Nirappil · AIID #5272
  3. RFK Jr.’s ‘Make America Healthy Again’ report seems riddled with AI slop
    theverge.com · Jess Weatherbed · AIID #5279

Who was involved

Alleged developer
Openai
Alleged harmed party
United States Department Of Health And Human Services, Scientific Rigor, Academic Integrity, General Public

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)

  • 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)

  • 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)

  • 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)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    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)

  • 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)

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