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AI incident #1252 ·

Judges in New Jersey and Mississippi Admit AI Tools Produced Erroneous Federal Court Filings

Open on the AI Incident Database 2 news reports Snapshot 7 Sep 2026

What happened

Two U.S. federal judges, Julien Neals (D.N.J.) and Henry Wingate (S.D. Miss.), reportedly admitted that staff in their chambers used AI tools, ChatGPT and Perplexity, to draft rulings containing purportedly fabricated quotes, cases, and parties. The erroneous filings were reportedly docketed before review, prompting Senate and judicial inquiries. Both rulings were retracted.

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 (2)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Judges Admit to Using AI After Made-Up Rulings Called Out (1)
    news.bloomberglaw.com · Justin Henry · AIID #6326
  2. Federal judges using AI filed court orders with false quotes, fake names
    washingtonpost.com · Daniel Wu · AIID #6325

Who was involved

Alleged developer
Openai, Perplexity Ai
Alleged harmed party
Plaintiffs And Defendants In Cormedix Securities Litigation, Plaintiffs And Defendants In Jackson Federation Of Teachers Et Al. V. Mississippi State Board Of Education, Julien Neals, Henry Wingate, United States District Court For The District Of New Jersey, United States District Court For The Southern District Of Mississippi, Epistemic Integrity, Judicial Integrity

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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