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Courtney Voyton v. Joseph Voyton, et al.

Court
M.D. Pa.
Jurisdiction
USA
Decided
2026-08-11
AI tool
Unidentified
Outcome
Court admonished plaintiff for AI misuse, required future affidavit disclosing AI use and citations checked, warned of sanctions (including striking pleadings/dismissal) for future unchecked AI-generated errors; complaint dismissed with leave to amend.
Monetary penalty
None reported

What was hallucinated

Misrepresented: Legal Norm | Plaintiff cited 42 Pa. Cons. Stat. § 5522 and described it as governing a government lawyer's professional responsibility, but the court found the statute actually concerns notice of intent to sue and that the plaintiff misrepresented the law. || Misrepresented: Doctrinal Work | Plaintiff's objections advanced legal doctrines (Younger abstention, Rooker-Feldman, quasi-judicial immunity) not discussed in the R&R—the court concluded these were 'ghost arguments' steered by generative AI and misleading.

Details

The district court found that the pro se plaintiff's filings were aided by generative AI and contained at least one clear hallucination: she misrepresented the substance of 42 Pa. Cons. Stat. § 5522 and advanced irrelevant 'ghost' doctrines (Younger abstention, Rooker-Feldman, quasi-judicial immunity) that the R&R never discussed. The court admonished the plaintiff, ordered future affidavits disclosing AI use and identification of sections drafted by AI, required certification that citations were checked, and warned that further unchecked AI misuse could result in sanctions up to striking pleadings and dismissing claims with prejudice.

Sanction teardown · M.D. Pa., USA · 2026-08-11

Courtney Voyton v. Joseph Voyton, et al.

What happened

In M.D. Pa., USA, a filing relied on an unnamed/unconfirmed AI tool to help draft legal argument. The court identified the following problems with the citations in that filing:

  • Misrepresented (Legal Norm)
    Plaintiff cited 42 Pa. Cons. Stat. § 5522 and described it as governing a government lawyer's professional responsibility, but the court found the statute actually concerns notice of intent to sue and that the plaintiff misrepresented the law.
  • Misrepresented (Doctrinal Work)
    Plaintiff's objections advanced legal doctrines (Younger abstention, Rooker-Feldman, quasi-judicial immunity) not discussed in the R&R—the court concluded these were 'ghost arguments' steered by generative AI and misleading.

Which AI tool

an unnamed/unconfirmed AI tool. Note: Charlotin's public database records tool attribution only where a court order, brief, or reporting on the matter states it explicitly; "unidentified" or "implied" means the record indicates AI use but does not name a specific product — we do not guess.

Outcome

Court admonished plaintiff for AI misuse, required future affidavit disclosing AI use and citations checked, warned of sanctions (including striking pleadings/dismissal) for future unchecked AI-generated errors; complaint dismissed with leave to amend.

Additional detail

The district court found that the pro se plaintiff's filings were aided by generative AI and contained at least one clear hallucination: she misrepresented the substance of 42 Pa. Cons. Stat. § 5522 and advanced irrelevant 'ghost' doctrines (Younger abstention, Rooker-Feldman, quasi-judicial immunity) that the R&R never discussed. The court admonished the plaintiff, ordered future affidavits disclosing AI use and identification of sections drafted by AI, required certification that citations were checked, and warned that further unchecked AI misuse could result in sanctions up to striking pleadings and dismissing claims with prejudice.

How Citation Safe would have caught this

Citation Safe runs three deterministic layers before a brief is filed: (1) does the citation exist against CourtListener's database of published opinions, (2) if quoted, does that exact language appear in the source, (3) does the cited case actually support the proposition it is cited for. Fabricated case citations fail Layer 1. Fabricated or misattributed quotations fail Layer 2 even when the underlying case is real. Misrepresented holdings — a real case cited for a proposition it does not support — are the target of Layer 3. None of these checks involve asking another language model whether the citation looks right; they are lookups and text-matches against the actual source, which is why a hallucinated citation has to survive a direct lookup against the authoritative source — not another model's opinion — to earn a VERIFIED stamp; our measured false-verify rate is published live at /quality.

Check a brief before you file it → · See our live false-verify rate

Source: https://www.damiencharlotin.com/documents/2844/Voyton_v._Voyton_USA_11_AUgust_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).

Source: https://www.damiencharlotin.com/documents/2844/Voyton_v._Voyton_USA_11_AUgust_2026.pdf

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