{"id":7523,"date":"2025-01-09T11:47:06","date_gmt":"2025-01-09T17:47:06","guid":{"rendered":"https:\/\/idsinc.com\/?p=7523"},"modified":"2026-03-05T16:41:40","modified_gmt":"2026-03-05T22:41:40","slug":"ai-hallucinations","status":"publish","type":"post","link":"https:\/\/idsinc.com\/en_gb\/ai-hallucinations\/","title":{"rendered":"Artificial Intelligence Did (Not) Help Me Write This Expert Report"},"content":{"rendered":"\n\n\n
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Executive Summary<\/strong><\/h3>\n\n\n\n

\u2014<\/strong><\/p>\n\n\n\n

Expert reporting requirements are dictated by the jurisdiction and venue of the underlying litigation. One of the universal truths however, is that an expert\u2019s report should be based on facts and sound methodology. Artificial Intelligence (AI) allows experts to perform their work faster and more efficiently, but that speed comes at a risk and a cost. At best, AI can make your access to information more efficient, but without experience and curiosity\u2014qualities that can transform that information into knowledge and wisdom\u2014experts risk making decisions that overlook deeper insights, or worse, simply being wrong by relying on \u201dfake\u201d  information. Not appreciating the limitations of AI and applying the appropriate peer-review standards to it may lead to the downfall of an expert, as it did in a matter in Minnesota.<\/p>\n<\/div>\n\n\n\n

Introduction<\/strong><\/h3>\n\n\n\n

AI tools are constantly improving in both their reliability and effectiveness. Tools like ChatGPT, Microsoft Copilot, and various open-source AI models are increasingly used by attorneys and experts alike. However, the limitations of these tools are regularly tested with each use by experts. A recent matter in Minnesota, surrounding new legislation intended to ban \u201cdeep fake\u201d technology used to influence elections offers critical insight into these limitations. An expert, known for his research and education on technology used for deception, has been accused of citing fabricated academic works in his declaration filed under penalty of perjury.<\/p>\n\n\n\n

This white paper aims to discuss the concept of AI hallucinations in expert reports to further the conversation and raise awareness on how to spot them, as well as how experts can avoid the pitfall of citing non-existent facts.<\/p>\n\n\n\n

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The Case Study: Kohls and Franson v. Ellison and State of Minnesota (\u201cMinnesota Deep Fake Litigation<\/em>\u201d)<\/strong><\/h2>\n\n\n\n

Background<\/strong><\/h3>\n\n\n\n

In the Minnesota Deep Fake Litigation<\/em>, Minnesota Attorney General (\u201cAG\u201d) Keith Ellison engaged Jeff Hancock, the founding director of Stanford University\u2019s Social Media Lab, to provide an expert declaration in support of the State\u2019s new legislation banning the use of \u201cdeep fake\u201d technology to influence elections. However, a recent Daubert motion filed by the Plaintiffs alleges that multiple academic studies cited by Hancock in support of his declaration are, in fact, hallucinations created by large language model (\u201cLLM\u201d) AI tools.<\/p>\n\n\n\n

Key Facts in the Daubert Motion<\/strong><\/h3>\n\n\n\n

Hancock has been challenged based on the following factors:<\/p>\n\n\n\n

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  1. AI Hallucination<\/strong>: The Daubert motion cites a believed \u201cAI Hallucination\u201d regarding a study relied upon by Hancock as a reason to bring the entire report under scrutiny.<\/li>\n\n\n\n
  2. Reputation In Question<\/strong>: The Daubert motion requests sanctions be brought on the Defendant and Hancock for \u201cfalse representation.\u201d<\/li>\n\n\n\n
  3. Previous Work Complicates Current Report<\/strong>: As part of most expert reporting requirements, an expert must provide their qualifications, including a listing of all testimonies and publications authored in the previous five years. Hancock\u2019s previous publications identify the potential harm of \u201cdeep fakes,\u201d yet he fell victim to nearly identical mistakes he identified in other authored works.<\/li>\n<\/ol>\n\n\n\n

    Analysis<\/strong><\/h3>\n\n\n\n

    The Daubert motion illustrates that litigants and their counsel are becoming increasingly aware of the rise of AI in legally filed documents, as well as the risks associated with it. One of the likely culprits for experts falling victim to hallucinations and other errors is certainly time constraints. However, if the time constraint is of the expert\u2019s own making, as opposed to being court-imposed, there is little excuse for the expert\u2019s mistakes.<\/p>\n\n\n\n

    As will be further discussed, there is a place for AI tools in an expert\u2019s work. However, this assistance from AI is best suited for the ideation and initial outlines of expert work product. Reliance on AI tools for significant portions of, or the creation of, final expert reports is still viewed as problematic by the larger legal community and comes with inherent risks.<\/p>\n\n\n\n

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    AI Hallucinations and Where They Come From<\/strong><\/h3>\n\n\n\n

    The underpinnings of the dispute and ultimate legislation involved \u201cdeep fakes\u201d in elections. \u201cDeep fakes\u201d are the product of humans using AI to create fabricated and misleading media. On its own, AI will not self-generate nor promulgate \u201cdeep fakes\u201d without the human element. Hallucinations, however, are self-generated results from LLMs due to ingesting incorrect information as the truth and generating a response to a query based on that. In almost a vicious, self-feeding cycle, the more misleading \u201cdeep fakes\u201d that exist, the more they can be fed into the LLMs, which then creates more hallucinations.<\/p>\n\n\n\n

    How To Spot and Hopefully Prevent Hallucinations<\/em><\/strong><\/h4>\n\n\n\n