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ELG20 v Minister for Immigration and Citizenship

Court
Federal Circuit and Family Court
Jurisdiction
Australia
Decided
2026-03-05
AI tool
ChatGPT; OpenAI
Outcome
Admonishment
Monetary penalty
None reported

What was hallucinated

Fabricated: Case Law | Applicant's written submissions cited numerous fictitious cases; Minister identified the fictitious citations and the Court declined to reproduce them. || False Quotes: Case Law | Applicant ascribed quotations to an existing case (SZATV) that do not appear in that case; Court noted the misattribution.

Sanction teardown · Federal Circuit and Family Court, Australia · 2026-03-05

ELG20 v Minister for Immigration and Citizenship

What happened

In Federal Circuit and Family Court, Australia, a filing relied on ChatGPT; OpenAI to help draft legal argument. The court identified the following problems with the citations in that filing:

  • Fabricated (Case Law)
    Applicant's written submissions cited numerous fictitious cases; Minister identified the fictitious citations and the Court declined to reproduce them.
  • False Quotes (Case Law)
    Applicant ascribed quotations to an existing case (SZATV) that do not appear in that case; Court noted the misattribution.

Which AI tool

ChatGPT; OpenAI. 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

Admonishment

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/1842/ELG20_v_Minister_for_Immigration_and_Citizenship_2026_FedCFamC2G_315_5_March_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).

Source: https://www.damiencharlotin.com/documents/1842/ELG20_v_Minister_for_Immigration_and_Citizenship_2026_FedCFamC2G_315_5_March_2026.pdf

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