Nelson Recruiting Insights  |  Mock Jury Research Guide

By the Nelson Recruiting Team  ·  Strategic Research. Real Results.

Generative AI-related lawsuits rose 81% year over year between January and April 2025 alone, and the median damages award for settled AI cases has already reached $4 million. Cases like Mobley v. Workday, where a federal court allowed claims to proceed against an AI hiring tool accused of discriminatory screening, are putting algorithmic decision-making in front of juries who have never had to evaluate anything like it before.

Software disputes, algorithmic bias claims, and medical cases built on complex clinical data are all landing in courtrooms at once, and every one of them depends on the same unglamorous question: can a jury actually understand the evidence well enough to reason about it during deliberation? A mock jury is the tool built to answer that question before trial. But a mock jury only answers it honestly if the panel sitting in the room reflects the real jury pool’s mix of technical literacy, not a version of it that is easier or harder than reality.

This guide covers what a mock jury actually tests when the evidence is technical, why comprehension testing fails when mock jury recruitment gets the panel wrong, and what legal teams should look for in a recruiting partner before their next AI, software, or medical case goes to trial.

Term	What It Means
Mock Jury	A research panel of surrogate jurors who review case materials and provide feedback before a real trial.
Mock Jury Recruitment	The process of finding and screening surrogate jurors so a mock jury panel authentically reflects the real trial venue’s jury pool.
Jury Comprehension	The degree to which jurors understand and can accurately apply the evidence presented to them during a trial.
Technical Evidence	Evidence that requires specialized knowledge to interpret, such as software code, AI model behavior, or medical and scientific data.
Comprehension Testing	Research conducted during a mock jury study to measure whether participants can understand and explain complex evidence during deliberation.

Why Jury Comprehension of Technical Evidence Is a Growing Problem

Jury comprehension of complex evidence is not a new concern. The American Bar Association’s Special Committee on Jury Comprehension was studying the issue as far back as 1989, and decades of academic literature have examined how well jurors absorb scientific and technical testimony. What has changed is the sheer volume and novelty of the evidence now reaching juries.

AI-related litigation has expanded rapidly. AI-related securities class action filings are on track to nearly double year over year, with 15 filed in just the first half of 2026 compared with 16 for all of 2025, and algorithmic bias claims, like the hiring discrimination allegations at the center of Mobley v. Workday, are testing whether juries can evaluate how a model actually made a decision. Software IP disputes, data breach litigation, and medical cases built on genomic or diagnostic algorithms raise the same underlying question in different forms.

Research on jury comprehension has found that when testimony becomes linguistically complex, jurors do not necessarily disengage, but they do shift how they evaluate it. One well-known study found that mock jurors relied more heavily on an expert’s credentials when the testimony itself was hard to follow, using the expert’s authority as a substitute for genuine understanding of the content. That is not a comprehension failure a legal team wants to discover for the first time at trial.

This shift matters more in technical cases than almost anywhere else in litigation, because credential-based deference cuts both ways. A jury that cannot follow the substance of an AI model’s decision logic may simply defer to whichever expert seems more credentialed or confident, regardless of which side actually has the stronger technical argument. That outcome has little to do with the merits of the case and everything to do with whether the panel could process the material in the first place, which is exactly what comprehension testing is designed to catch before it happens for real.

By the Numbers Generative AI-related lawsuits increased 81% year over year between January and April 2025, with median damages on settled AI cases reaching $4 million, according to insurtech Testudo’s litigation tracking. Source: Testudo, via Reuters/Munich Re litigation analysis, 2025

What a Mock Jury Actually Tests When the Evidence Is Technical

A mock jury is a research panel of surrogate jurors, recruited to reflect the demographics of the real trial venue, who review case materials, evidence, and witness testimony before a real trial takes place. When the underlying evidence is technical, the study is not just measuring which side the panel favors. It is measuring whether the panel can accurately explain the evidence back, in their own words, during deliberation.

That distinction matters because comprehension and persuasion are not the same thing. A panel can reach a verdict without ever correctly understanding the mechanism behind an AI model’s decision or the clinical significance of a lab result. A well-designed mock jury study is built to catch that gap, tracking not just the outcome but the reasoning jurors use to get there.

Research on jury deliberation offers a useful insight here. Legal scholarship on jury decision-making has found that an individual juror with a stronger grasp of scientific evidence can explain its meaning and significance to the rest of the panel during deliberation, increasing the group’s overall ability to weigh that evidence correctly, a pattern also observed in a mock jury experiment centered on complex mitochondrial DNA testimony. Group deliberation can partially correct for individual comprehension gaps, but only if the panel actually contains a realistic mix of jurors, some who grasp the material quickly and some who need it explained. That mix is not something a mock trial can manufacture after recruiting is already done.

This is why a strong comprehension test looks past the final verdict entirely. Post-deliberation debriefs, where each participant is asked to explain the core technical concept in their own words, often reveal more than the verdict itself. A panel that reaches the right outcome for the wrong reasons is just as much a warning sign as a panel that reaches the wrong outcome, because it means the legal team’s explanation of the evidence is not actually the thing driving the result.

The Recruiting Flaw That Quietly Invalidates Comprehension Testing

Most legal teams commissioning a mock jury for a technical case are focused on the case materials: how to present the AI model’s behavior, how to explain the software architecture, how to walk a panel through clinical data. Far fewer are thinking critically about who is actually sitting on the panel, and that is where comprehension testing quietly goes wrong.

A panel recruited with too many technically fluent participants, engineers, IT professionals, or healthcare workers, will understand the evidence more easily than a real jury would. The study comes back looking clean, the legal team feels confident, and that confidence is not earned. The opposite failure is just as damaging. A panel with little exposure to the relevant subject matter may struggle with material a real, mixed jury would actually process just fine, making a strong case look weaker than it is.

Either failure produces the same result: a mock jury finding that does not predict what will actually happen at trial. The fix is not a better discussion guide or a clearer visual aid. It is a recruiting process built to mirror the real venue’s mix of education, occupation, and technical exposure from the very first candidate contacted.

Why Mock Jury Recruitment Determines Whether Comprehension Testing Means Anything

Mock jury recruitment is the step that actually determines whether a comprehension test is valid, not the moderation, not the case presentation, and not the analysis that follows. A study is only as reliable as the panel’s resemblance to the real jury pool that will eventually hear the case.

For technical cases specifically, that means recruiting has to go beyond the standard demographic mix of age, gender, and ethnicity, though those still matter. It also has to account for the spread of education levels and occupational backgrounds a real jury pool in that venue would actually contain, so the panel includes people who will need the AI model or the medical data explained clearly, alongside people who will grasp it quickly. A panel skewed in either direction produces a comprehension test that measures the wrong thing.

This is also where conflict screening becomes more nuanced for technical cases. A participant who works in software development or has a background in the exact medical specialty at issue may need to be excluded, or deliberately balanced against participants without that background, depending on what the real jury pool is likely to look like in that venue.

Three Categories of Technical Evidence Where Comprehension Testing Matters Most

Software and Algorithmic Evidence

Cases involving AI hiring tools, pricing algorithms, and automated decision systems are testing whether juries can evaluate how a model actually made a decision, not just whether the outcome seemed unfair. California’s AB 325 lowered the pleading standard for algorithmic pricing claims in 2026, and cases like Mobley v. Workday have opened the door to holding technology vendors, not just the companies using their tools, directly liable. Comprehension testing here focuses on whether a mock jury can follow an explanation of how an algorithm weighs inputs, without collapsing into a simple “the computer decided” narrative that skips the actual mechanism.

Intellectual Property and Data Breach Litigation

Software IP disputes and data breach cases often hinge on technical distinctions, how code was structured, what data was actually exposed, whether a security control functioned as claimed, that are easy for experts to describe and hard for a lay jury to hold onto through a multi-day trial. Comprehension testing in these cases focuses on retention as much as initial understanding: can the panel still accurately explain the key technical distinction during deliberation, after days of testimony on other issues.

Medical and Scientific Evidence

Medical malpractice, product liability, and pharmaceutical litigation frequently turn on causation evidence built from clinical data, statistical analysis, or diagnostic algorithms. This is the category with the deepest body of academic research behind it, and the findings are consistent: jurors generally attempt to evaluate expert testimony on its merits rather than deferring blindly to credentials, but only when the evidence is presented in a way they can actually process. A mock jury built with the right mix of participants reveals whether that presentation is working before a real jury has to make the same call with a client’s outcome on the line.

What to Look For in a Mock Jury Recruiting Partner for Technical Cases

Comprehension testing raises the bar for what a recruiting partner needs to deliver. A few questions separate a firm equipped for this work from one that is not:

• Can they recruit for occupational and educational diversity, not just standard demographics? A panel needs a realistic spread of technical fluency, not just age and gender balance.
• Do they screen for relevant professional background as a variable, not just a conflict check? Participants with direct expertise in the technology or medical specialty at issue may need to be excluded or deliberately balanced.
• Can they recruit to the actual trial venue? A comprehension test built on a national online panel does not reflect what a specific jurisdiction’s jury pool actually looks like.
• Do they have experience recruiting for legal research specifically? Consumer panel experience does not translate automatically to the vetting rigor legal research requires.

How Nelson Recruiting Supports Mock Jury Recruitment for Technical Cases

Nelson Recruiting has supported mock jury recruitment for over 45 years, building a proprietary database of more than 1.5 million participants and applying the expertise behind 700+ legal recruitments to cases across every type of litigation, including the technical and scientific cases now reaching courtrooms at a growing pace.

Every mock jury panel we build is recruited to reflect the actual trial venue, considering age, gender, ethnicity, political perspective, education, and occupational background, so legal teams get a comprehension test that means something, not a panel that happens to be easier or harder to convince than the jury they will actually face. Our screening process identifies and manages participants with relevant technical or medical backgrounds deliberately, rather than leaving that variable to chance.

Every project follows the same five-step process: kickoff, targeted outreach, pre-qualifying and vetting, confirmation, and ongoing support through the study itself, built to deliver a panel legal teams can actually trust their trial strategy to.

Preparing a case with complex technical evidence? Contact hello@nelsonrecruiting.com to request a bid on mock jury recruitment.

Sources

Testudo, via Reuters/Munich Re litigation analysis — AI-Related Lawsuit Growth, 2025
Best Lawyers / Best Law Firms — “California AI Lawsuits 2026: Pricing, Hiring and Uber Cases,” July 2026
InvestmentNews — “AI Lawsuits Surge to Dominate Securities Class Action Filings in 2026,” 2026
Cooper, Bennett, & Sukel — Mock Juror Response to Technical Expert Testimony, 1996, via ResearchGate
Valerie P. Hans — “Juries Judging Science,” Columbia Science & Technology Law Review, 2024
Daedalus / MIT Press — “Improving Judge & Jury Evaluation of Scientific Evidence,” 2018
PMC / ScienceDirect — “Juror Comprehension of Forensic Expert Testimony: A Literature Review and Gap Analysis”
American Journal of Public Health — “Expert Evidence, the Adversary System, and the Jury”
Nelson Recruiting — Market Research for Strategic Participant Recruiting Brochure, 2026
Nelson Recruiting — “How Mock Juries Predict Jury Decisions,” 2026