Bhagat v. Shah: SDNY Magistrate Expressly Finds ‘AI-Generated Hallucination’ — Admonishes Pro Se Defendant

Background: A Complex Civil Dispute and a Voluminous Docket

The underlying civil litigation between Viral Bhagat and Anuja Sharad Shah had generated a voluminous docket by April 2026 — the court’s order references docket entries in the 600s, reflecting years of active litigation and numerous prior motions. The case had already seen an attorney sanctioned for unprofessional communications. With pro se defendant Shah navigating a complex case, AI-assisted brief drafting became an attractive option — with the predictable consequence of AI hallucination.

The AI issue emerged in the context of a discovery dispute. Shah cited In re SunEdison Sec. Litig., 300 F. Supp. 3d 444 (S.D.N.Y. 2018), for the proposition that “Courts are reluctant to compel disclosure of internal corporate documents that implicate trade secrets, competitively sensitive information, or non-party confidentiality obligations.” This is a plausible-sounding legal proposition. The case cited is a real SDNY case. But the quoted text does not appear in that case — which addressed a motion to dismiss for failure to state a claim, not a document withholding dispute.

The AI Issue: A Real Case, a Fabricated Quotation

Magistrate Judge Tarnofsky did something courts increasingly do: she searched the quoted text not just in the cited case but across Westlaw’s full database. The quoted language did not appear in In re SunEdison Sec. Litig. — and it did not appear anywhere on Westlaw. The judge’s conclusion was direct and unequivocal: “I therefore conclude that the citation is an AI-generated hallucination.”

This is one of the clearest judicial statements in the developing AI hallucination case law. Most courts say a citation is “fabricated” or “incorrect.” Judge Tarnofsky said it is an “AI-generated hallucination” — identifying the tool and the mechanism in the judicial record. This precision matters: it creates a clear, searchable term for future reference and signals that courts are willing to name AI explicitly as the cause of citation errors.

The order also acknowledged the practical reality facing pro se litigants: “I understand the difficulties of litigating pro se and the reasons why a pro se litigant might choose to use AI-generated filings.” This empathetic framing is notable — the court acknowledged the resource disparity that makes AI tools attractive to unrepresented parties, while still holding them to the verification standard.

Holdings

1. Citation identified as AI-generated hallucination. The court expressly found that the quotation attributed to In re SunEdison Sec. Litig. was not in that case, was not anywhere on Westlaw, and was an AI-generated hallucination.
2. Admonishment issued; no monetary sanction. Given pro se status and the court’s acknowledgment of the difficulties of self-representation, the court issued an admonishment rather than monetary sanctions.
3. Verification duty stated clearly. “Pro se litigants are obligated to review the cases cited in their filings to confirm that they exist and stand for the proposition for which they are cited.” This is now a standing order-level statement in the case.
4. Separate matters also addressed in same order. The order resolved multiple pending items in a complex, high-volume docket, demonstrating that AI hallucination issues arise in the middle of complex ongoing litigation, not just in discrete sanction proceedings.

“I therefore conclude that the citation is an AI-generated hallucination. I understand the difficulties of litigating pro se and the reasons why a pro se litigant might choose to use AI-generated filings. However, Defendant is admonished that pro se litigants are obligated to review the cases cited in their filings to confirm that they exist and stand for the proposition for which they are cited.”

— Magistrate Judge Robyn F. Tarnofsky, S.D.N.Y., April 6, 2026

India Angle: Judicial Empathy for Pro Se AI Use in Indian Courts

The Bhagat v. Shah order is one of the few AI hallucination decisions that explicitly acknowledges why pro se litigants use AI tools — the resource disparity that makes professional legal research inaccessible. This is acutely relevant in India, where access to justice remains a significant challenge and self-represented litigants are commonplace, particularly in consumer forums, family courts, revenue courts, and lower civil courts.

Relevant Indian Law

• Access to justice and the litigant in person: India’s Supreme Court has repeatedly held that the right to access courts is a fundamental right (Article 21, Constitution of India). Pro se litigants — called “litigants in person” — are entitled to judicial latitude but not exemption from procedural norms. An AI-generated hallucination in an Indian pro se application could still be treated as a misrepresentation to the court, inviting contempt or cost orders.
• Consumer Protection Act 2019: Consumer forums are accessible without lawyers, making them a likely first venue for AI-assisted pro se filing in India. A fabricated citation in a consumer complaint or reply would be particularly problematic because consumer forums often accept written submissions at face value without opposing counsel scrutiny.
• BCI Rules for advocates assisting pro se litigants: When an advocate provides limited assistance to a pro se litigant (e.g., drafting a petition for filing by the client), the advocate should ensure the document does not contain AI hallucinations. The “ghost drafting” responsibility raises difficult questions about who bears the verification duty when an advocate is not the filing party.

Three Practical Tips for Indian Practitioners

1. When a client produces an AI-generated draft, verify all citations before adopting it. If a client brings an AI-drafted petition or affidavit, treat every citation as unverified. Do not adopt AI-generated text without independently confirming each citation against the original judgment. The advocate who signs the vakalatnama bears professional responsibility for the accuracy of all court filings.
2. Teach clients about the verification duty before they use AI tools. When advising self-represented clients (or clients who will file themselves, e.g., in consumer forum or labour tribunal proceedings), explicitly tell them: any case citation generated by AI must be verified against the official reporter or an authenticated online database before it appears in a filing. Make this part of your standard client intake for matters where the client will file pro se.
3. Search the quoted text, not just the case name. Judge Tarnofsky’s technique — searching the quoted text across the entire Westlaw database — is reproducible on SCC Online and Manupatra for Indian judgments. If an AI-generated quotation attributed to a Supreme Court case cannot be found in a full-text search on SCC Online, it is almost certainly fabricated. This targeted search takes seconds and can save a career.

Quick Takeaways

• A court has now used the term “AI-generated hallucination” directly in a published order, naming the technology and its mechanism — creating a precise new term of art in AI sanctions case law.
• Searching the quoted text across a full database (not just in the cited case) is the most reliable technique to identify AI-fabricated quotations — and courts are now doing this routinely.
• Judicial empathy for pro se AI use does not translate into exemption from the verification duty — it merely explains why courts start with admonishment rather than monetary sanctions.
• The verification obligation applies even when a case is real and its citation format is correct — a real case with a fabricated quotation violates Rule 11 just as surely as a nonexistent case.
• In India: AI-generated quotations from Supreme Court cases are unverifiable by searching Indian Kanoon and SCC Online — use these tools before any citation from an AI-generated document reaches the court record.

Deep Dive: The “AI-Generated Hallucination” Label and Its Legal Significance

The precise language in Bhagat v. Shah — “I therefore conclude that the citation is an AI-generated hallucination” — deserves careful analysis. Courts in earlier cases used terms like “fabricated,” “non-existent,” “incorrect,” or “unverifiable” to describe bad citations. Judge Tarnofsky’s formulation is more specific: she names the tool (AI), names the mechanism (hallucination), and draws a firm conclusion (“I therefore conclude”). This is not mere judicial commentary — it is a factual finding in a published order.

The legal significance is two-fold. First, the finding creates a precedent that courts can make factual determinations about AI tool use based on circumstantial evidence — the characteristics of the citation itself, specifically the presence of a real case citation with a quotation that does not appear in that case or anywhere in the legal database. No admission by the litigant is required; the nature of the error is sufficient. Second, the label “AI-generated hallucination” is now an established judicial term, searchable in legal databases, and available as authority in future cases.

For Indian practitioners, the implications are profound. If an Indian advocate submits a brief containing a quotation attributed to a real Supreme Court case that cannot be found in that case, an opposing advocate who does the same Westlaw/SCC Online search can — and should — bring this to the court’s attention and seek a finding comparable to Bhagat v. Shah. The Indian Contempt Act’s broad definition of contempt, combined with the BCI’s misconduct jurisdiction, would support proceedings against an advocate who knowingly or recklessly submitted an AI-generated fabricated quotation.