{"id":905830,"date":"2026-06-24T14:51:24","date_gmt":"2026-06-24T20:51:24","guid":{"rendered":"https:\/\/www.myconstructionexpert.com\/blog\/?p=905830"},"modified":"2026-06-24T14:51:29","modified_gmt":"2026-06-24T20:51:29","slug":"expert-witness-ai-prompts-what-litigators-need-to-know","status":"publish","type":"post","link":"https:\/\/www.myconstructionexpert.com\/blog\/expert-witness-ai-prompts-what-litigators-need-to-know\/","title":{"rendered":"Court Orders Disclosure Of Expert Witness\u2019s AI Prompts: What Litigators Need To Know"},"content":{"rendered":"\n

Matthew D. Provance, Amber C. Thomson, Scott H. Kaiser, Katlyn Barry and Celeste Gomez | Mayer |Brown<\/a><\/p>\n\n\n\n

On May 18, 2026, Magistrate Judge Thomas O. Farrish of the US District Court for the District of Connecticut ordered the plaintiff in\u00a0Conservation Law Foundation, Inc. v. Shell Oil Company, et al.\u00a0<\/em>(Case No. 3:21-cv-00933, D. Conn.), to produce the generative AI prompts<\/a> that its expert witness, Dr. Naomi Oreskes, used in preparing her expert report. (ECF No. 970). This appears to be the first federal court decision requiring an expert witness to disclose AI prompts as part of discoverable methodology for the expert\u2019s opinions. The district court has stayed the order pending resolution of plaintiff\u2019s objection.<\/p>\n\n\n\n

Background<\/h4>\n\n\n\n

The plaintiff, Conservation Law Foundation (CLF), retained Dr. Oreskes as an expert on the history of climate change science. In preparing her expert report, Dr. Oreskes and her research assistant, Dr. Kaurov, used a commercially available generative AI tool to filter and identify potentially relevant documents from the defendants\u2019 document productions. Dr. Oreskes disclosed her use of AI in her expert report, served on May 8, 2025, identifying certain search terms and frequencies. Defendants subsequently sought additional information about the AI prompts and system outputs through Dr. Oreskes\u2019 deposition on September 9, 2025, and various informal requests.<\/p>\n\n\n\n

After extended meet-and-confer efforts failed to resolve the dispute, Defendants filed a motion to compel. On May 18, 2026, Magistrate Judge Farrish granted the motion, finding that \u201c[a]n expert witness\u2019s methodology is fair ground for discovery,\u201d and that \u201cthe process by which Dr. Oreskes culled down the defendants\u2019 document production into a subset to be worked with is an aspect of that methodology.\u201d The court rejected CLF\u2019s argument that a stipulation protecting expert \u201cnotes, drafts, or communications\u201d shielded the prompts, holding that the agreement was not \u201cquite clear\u201d enough to cover AI prompts specifically. Finally, the court found that defendants had an \u201cevidence-backed reason\u201d for doubting CLF\u2019s representation that no additional materials existed, because the expert\u2019s research assistant had referenced \u201cprompt[s]\u201d in his own declaration.<\/p>\n\n\n\n

Pending Objection<\/h4>\n\n\n\n

On June 2, 2026, CLF filed an objection to the Magistrate Judge\u2019s order under 28 U.S.C.
\u00a7 636(b)(1)(A) and Fed. R. Civ. P. 72(a), arguing the order is \u201cclearly erroneous or contrary to law\u201d on multiple grounds, including: (1) the court applied the broad Rule 26(b) relevance standard rather than the narrower expert disclosure requirements of Rule 26(a)(2)(B)(ii); (2) no formal Rule 33 or Rule 34 requests were ever served, so Rule 37 cannot enforce compliance; (3) the Rule 29 stipulation expressly protects such iterative research materials; and (4) the motion was untimely and overlaps with defendants\u2019 pending Daubert<\/em> challenge. CLF further argues that the sole precedent cited, Macchia v. ADP<\/em>, 711 F. Supp. 3d 162 (E.D.N.Y. 2024), involved fact, not expert, discovery. The district court judge has stayed the order pending resolution of CLF\u2019s objection. The matter remains pending.<\/p>\n\n\n\n

Broader Implications for Expert Discovery<\/h4>\n\n\n\n

This order, if upheld, could meaningfully expand the scope of expert discovery. By treating AI prompts as discoverable methodology, rather than as the functional equivalent of research assistant communications or keyword searches, the court has signaled that any AI-assisted step in the report preparation process may be subject to disclosure, regardless of whether the expert actually \u201cconsidered\u201d the AI outputs in forming opinions or used the tool only for preliminary document culling. The practical effect is to place AI prompts in the same category as dataset selection criteria or statistical model parameters\u2014<\/strong>aspects of methodology that have long been fair game, rather than treating them as protected preliminary research. The precedential basis for this expansion is thin.<\/p>\n\n\n\n

The ruling also raises the prospect of increasingly granular discovery into AI-assisted workflows. If prompts are discoverable, parties may face broad demands for prompt histories, iterative refinements, system instructions, and chat logs, even when such materials were not preserved in the ordinary course. This could create significant burdens, particularly where AI tools are used interactively and iteratively, and courts will need to develop frameworks for distinguishing between AI interactions that genuinely inform an expert\u2019s methodology, and those that are more analogous to preliminary, discarded research\u2014<\/strong>a line that existing precedent on \u201cfacts or data considered\u201d under Rule 26(a)(2)(B)(ii) does not clearly draw.<\/p>\n\n\n\n

Practical Recommendations<\/h4>\n\n\n\n

In light of this development, outside counsel, in-house teams, and experts should consider the following proactive steps.<\/p>\n\n\n\n