Compiled by Citation Safe (Digital Empire Holdings LLC) from the court’s published sanction order (decided August 24, 2026). Quotes and case details come from the order itself — check the primary source before relying on them.

Sanction teardown · CA Minnesota, USA · 2026-08-24

In the Matter of an Investigation into Implementing Changes to the Renewable Energy Objectives and the Newly Created Carbon-Free Standard Under Minn. Stat. § 216B.1691

What happened

In CA Minnesota, 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)

Counsel's brief cited two nonexistent cases. The opinion notes the problem but does not identify the fabricated case names or citations; counsel attributed the errors to lack of attention to detail and denied that they were AI hallucinations.
Misrepresented (Case Law)

Counsel's brief misquoted cases that did exist. The opinion does not identify the specific cases or quotations and records counsel's acknowledgment of responsibility for the errors.

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

No sanction imposed

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/3213/In_re_Investigation_into_Implementing_Changes_USA_24-08-2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).