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X. v. Canada

Court
D. DC
Jurisdiction
USA
Decided
2026-01-16
AI tool
Implied
Outcome
Adverse Costs order
Monetary penalty
None reported

What was hallucinated

Fabricated: Case Law | Court noted plaintiff frequently included direct quotations from case law that do not appear in the cited decisions (citing ECF No. 111 n.4 as examples). || False Quotes: Case Law | Plaintiff attributed a quotation to Chambers v. NASCO, Inc. that does not appear in that Supreme Court opinion; the court identified the quoted language in Mot. at 12 as absent from Chambers.

Sanction teardown · D. DC, USA · 2026-01-16

X. v. Canada

What happened

In D. DC, 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:

  • Fabricated (Case Law)
    Court noted plaintiff frequently included direct quotations from case law that do not appear in the cited decisions (citing ECF No. 111 n.4 as examples).
  • False Quotes (Case Law)
    Plaintiff attributed a quotation to Chambers v. NASCO, Inc. that does not appear in that Supreme Court opinion; the court identified the quoted language in Mot. at 12 as absent from Chambers.

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

Adverse Costs order

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.

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Source: Damien Charlotin's public AI Hallucination Cases Database (CC0).

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