Sanction teardown · Income Tax Appellate Tribunal (ITAT), Bangalore, India · 2024-12-30
Buckeye Trust v. PCIT
What happened
In Income Tax Appellate Tribunal (ITAT), Bangalore, India, 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:
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Misrepresented (Legal Norm)Verify statutory provisions as cited and amend citation appropriately.
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Outdated Advice (Repealed Law)Outdated provision invoked: Tribunal cites section 164A as the charging provision for oral trusts.
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Misrepresented (Case Law)Case mis-cited with inconsistent court/citation: attributed to Bombay High Court but shown as '(Mad)'.
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Misrepresented (Case Law)Party name misstated in Rajasthan HC citation as 'Every stone' instead of 'Emery Stone'.
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Misrepresented (Legal Norm)Definition of 'property' under section 56(2)(x) reduced to only 'shares and securities', ignoring other categories expressly listed in the statute.
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
Judgment was retracted and case re-heard
Additional detail
Seemingly, the judge cited back hallucinated authorities invoked by one counsel. The Judgment was later reportedly withdrawn.
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: https://www.damiencharlotin.com/documents/477/Buckeye_Trust_v._PCIT_India_30_December_2024.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).