Judge Paul W. Grimm and Dr. Maura R. Grossman submitted a letter in support of the proposed Federal Rule of Evidence 707. Here are some highlights.

The four-page letter, provided to me by Dr. Grossman and available here, was dated yesterday and addressed to the Advisory Committee on the Federal Rules of Evidence.

Judge Grimm (ret.) and Dr. Grossman positioned their letter submission as a counterpoint to numerous negative comments the Committee had previously received. Their goal was to demonstrate why the proposed rule would be helpful in managing the “panoply of problems” presented by evidence generated by artificial intelligence, specifically concerning authentication and the rise of fabricated content from generative AI.

In the letter, Grimm and Grossman identified three distinct evidentiary issues related to AI that necessitate revisions to the Federal Rules of Evidence:

1. Acknowledged AI-Generated Evidence: This concerns the proper authentication of evidence that all litigants acknowledge was created by an AI application.
2. Unacknowledged AI-Generated Evidence (i.e., Deepfakes): This addresses the admissibility of evidence where one party claims it is authentic, while the opposing party contends it is a fabrication generated by AI.
3. Admissibility Standards and Witness Qualification: This involves determining the appropriate standards for admitting acknowledged AI-generated evidence – which is inherently scientific or technical – and identifying the type of witness properly qualified to provide the foundational testimony for its admission.

The authors noted their prior work has addressed the first two issues through proposed amendments to FRE 901, but their current commentary focuses on the value of the proposed Federal Rule of Evidence 707 (FRE 707) in addressing the third issue.

In the letter, Grimm and Grossman pushed back on critics of proposed FRE 707 by arguing that its reliance on FRE 702 is not only appropriate but essential. They contended that AI-generated evidence raises the same core admissibility concern that has long governed expert testimony: reliability. Rather than viewing FRE 707 as an awkward extension of existing doctrine, they framed it as a pragmatic and principled way to integrate AI evidence into the Federal Rules of Evidence. Their arguments included:

Leveraging the Established Framework of Rule 702: First, they asserted that AI-generated evidence is inherently technical and well beyond the common understanding of lay jurors and often judges. Because such evidence depends on scientific or specialized processes, they argued that FRE 702 is “purpose-made” to evaluate its admissibility. The recent amendments to Rule 702 reinforce this point by sharpening the judicial focus on the validity and reliability of the underlying methodology, which Grimm and Grossman view as precisely what AI evidence demands.

Practical Application and Judicial Management: Grimm and Grossman also highlighted the practical advantages of leveraging an established evidentiary framework rather than creating a new stand-alone rule. FRE 702 is already familiar to courts and practitioners, offering immediate guidance and predictability. This, they argued, is far preferable to the slow and deliberative process of crafting entirely new evidentiary standards, especially given the rapid pace at which AI technologies are evolving.

Preventing Circumvention of Expert Witness Standards: Grimm and Grossman also rejected concerns that FRE 707 would require a full Daubert hearing in every case involving AI. They express confidence in judicial case management, noting that courts routinely control evidentiary complexity through early disclosures, targeted discovery, and pretrial motion practice. In their view, these tools allow judges to assess AI evidence efficiently without overburdening litigation or transforming every case into a technical showdown.

Integration with Existing Authentication Rules: Grimm and Grossman also identified a key benefit of FRE 707 in preventing parties from circumventing expert-witness standards. By requiring a FRE 702-level foundation even when AI evidence is introduced without an expert, the rule closes a common loophole in which proponents rely on witnesses who can operate a technology but lack knowledge of its development, testing, or error rates. Finally, they noted that FRE 707 would integrate seamlessly with existing authentication rules – specifically FRE 902(13) and (14) – by ensuring that certifications used to self-authenticate AI-generated evidence also satisfy Rule 702’s reliability requirements.

Grimm and Grossman did agree with critics on one point: the phrase “machine-generated” is not defined in the rule and is considered a source of potential confusion. To resolve this ambiguity, they proposed amending the rule to define its scope more precisely, suggesting Maryland Rule 2-504.3 (which defines “computer-generated evidence” and provides specific exclusions) as a model for how this could be achieved. They also noted that using the term “artificial intelligence” instead of “machine-generated” would most effectively avoid any confusion as to the proposed rule’s intended scope.

You can check out the entire letter here. And here’s a link to the letter on the Regulations.gov site. It will be interesting to see to what extent the rules are eventually modified to address AI-generated evidence. Much more debate to come, I suspect. Thanks to Maura for sharing the letter with me as it is evidently not posted on the government site yet.

So, what do you think? Do you agree with their points in support of the proposed Federal Rule of Evidence 707? Please share any comments you might have or if you’d like to know more about a particular topic.

Image created using Microsoft Designer, using the term “robot lawyer looking through a large book titled ‘Rules of Evidence’”.

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