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Wu Leshi v Cheung Long Yin

Court
Lands Tribunal
Jurisdiction
UK
Decided
2026-03-20
AI tool
Implied
Outcome
The Tribunal rejected the legal assertion
Monetary penalty
None reported

What was hallucinated

Misrepresented: Legal Norm | The respondent relied on section 160 of the Crimes Ordinance as the statutory provision for common assault. The Tribunal clarified that section 160 concerns loitering and that common assault is governed by section 40 of the Offences Against the Person Ordinance, stating that the respondent was likely misled by AI.

Sanction teardown · Lands Tribunal, UK · 2026-03-20

Wu Leshi v Cheung Long Yin

What happened

In Lands Tribunal, UK, 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)
    The respondent relied on section 160 of the Crimes Ordinance as the statutory provision for common assault. The Tribunal clarified that section 160 concerns loitering and that common assault is governed by section 40 of the Offences Against the Person Ordinance, stating that the respondent was likely misled by AI.

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

The Tribunal rejected the legal assertion

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/3157/LDPD001041_2025.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).

Source: https://www.damiencharlotin.com/documents/3157/LDPD001041_2025.pdf

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