AIPolicyTracker
Menu

AI incident #1447 ·

Sixth Circuit Sanctioned Lawyers in Whiting v. City of Athens over Alleged Fake Appellate Citations in Briefs Reportedly Bearing Hallmarks of Hallucinations

Open on the AI Incident Database 4 news reports Snapshot 7 Sep 2026

What happened

The U.S. Court of Appeals for the Sixth Circuit sanctioned attorneys Van Irion and Russ Egli after reportedly finding more than two dozen fake citations and alleged factual misrepresentations in appellate briefs in Whiting v. City of Athens. The court asked whether generative AI had been used, but the attorneys reportedly did not answer that question. Public reporting described the filings as bearing hallmarks of AI hallucinations.

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 (4)

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

  1. US appeals court fines lawyers $30,000 in latest AI-related sanction
    reuters.com · Mike Scarcella · AIID #7128
  2. Sixth Circuit Sanctions Attorneys for Fake Citations – What Does This Mean for Use of AI?
    sixthcircuitappellateblog.com · Katie Brown · AIID #7444
  3. Sixth Circuit Sanctions Attorneys for Fake Citations – What Does This Mean for Use of AI?
    natlawreview.com · Kathryn (Katie) M. Brown · AIID #7129
  4. Fabricated Citations, Real Consequences: The Sixth Circuit’s Multi-Pronged Response to Attorney Misconduct
    reminger.com · Rafael P. McLaughlin · AIID #7445

Who was involved

Alleged deployer
Van Irion, Russ Egli
Alleged harmed party
U.S. Court Of Appeals For The Sixth Circuit, City Of Athens Tennessee, 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)

Incidents in the same risk subdomain

All incidents in this subdomain