Compiled by Citation Safe (Digital Empire Holdings LLC) from the court’s published sanction order (decided September 22, 2026). Quotes and case details come from the order itself — check the primary source before relying on them.

Sanction teardown · D. D.C., USA · 2026-09-22

Garner v. Transit Employees Federal Credit Union

What happened

In D. D.C., 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:

False Quotes (Case Law)

Counsel quoted Aka for propositions that intent and credibility are crucial jury issues, that comparators need not be mirror images, and that discriminatory intent often must be inferred from circumstantial evidence; the court stated those propositions do not appear in Aka.
False Quotes (Case Law)

Counsel attributed to Waterhouse a statement that statements revealing discriminatory motivation are highly probative of discriminatory intent; the court stated that the D.C. Circuit did not make that statement in the opinion.
False Quotes (Case Law)

Counsel quoted Brown for the proposition that employees may be similarly situated despite reporting to different supervisors when the same decision-makers participated in the adverse actions; the court stated that language is not in Brown.

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

Order to Show Cause

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/3136/GARNER_v_TRANSIT_EMPLOYEES_FEDERAL_CREDIT_UNION_USA_22_September_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).