Explaining Algorithms to Judges and Juries in Marketing Cases

The algorithm is often the silent party

A surprising number of marketing disputes hinge on a system that is not a party to the case. A search engine ranked one business above another. An autocomplete box suggested a damaging phrase. A social feed showed a copied video to millions of people. Litigants often assume that a person must have arranged these results, when the more ordinary explanation is that an automated system did what it was designed to do.

An ai search expert witness is retained to explain those systems to people who do not work with them, and to test competing explanations against the data. That work cuts both ways: sometimes the data shows ordinary behaviour, and sometimes it shows a pattern that ordinary behaviour does not explain.

Explain the mechanism, then show the evidence

Effective explanation has two parts. The first is a plain-language account of how the relevant system generally works: that search results are ranked from many signals, that autocomplete reflects patterns in what people search for, and that social feeds distribute content according to engagement and predicted interest. The second part is evidence: data from the matter itself showing whether the observed results fit that ordinary behaviour or depart from it.

International Metaphysical Ministry, Inc. v. Wisdom of the Heart Church — U.S. District Court, Northern District of California (2025). Trademark and digital competition. Testimony refuted allegations of SEO manipulation by demonstrating how Google search and autocomplete algorithms work, showing with data that the observed results were consistent with organic behaviour rather than intentional deception. The matter ended in a dismissal with prejudice following resolution.

Honesty about the limits

No outside expert has access to the full internal workings of a major search engine or social platform. Credible testimony says so. The defensible position is to describe publicly documented behaviour, test it against observable data, and distinguish clearly between what the data shows and what is inference. Overstating certainty is one of the fastest ways for an expert opinion to lose weight under cross-examination.

• Separate published platform documentation from personal inference.
• Use the matter's own data wherever it exists.
• Show alternative explanations and why the data does or does not support them.
• Avoid technical jargon that the finder of fact cannot evaluate.

Teaching is a skill in its own right

Explaining technical systems to non-specialists is a learned skill. Earlier in his career, at LexisNexis / Martindale-Hubbell, Dan Stratford educated attorneys and sales teams on digital marketing through one-on-one consulting, internal training and legal education programmes across multiple U.S. states. He has 29 years in practice and has been deposed or has testified two times in the last four years. A third-party directory reference is the AI expert witness listing on AI List Pal.

The same principle applies well beyond the courtroom. Law firms that publish plain-language explanations of complex topics tend to build more trust with prospective clients; an article on why educational content matters in legal marketing discusses that idea as general further reading for firms.

Visuals, timelines and demonstrations

Algorithms are easier to understand when they can be seen. Useful aids include timelines that line up ranking or reach changes with known events, side-by-side comparisons of search results gathered under controlled conditions, and simple charts showing how engagement correlated with distribution. Each aid should be traceable to the underlying data so it can be checked by the other side.

Demonstrations should be modest. A live search performed in a courtroom is shaped by that day's index, the room's location and the device in use, so it may not reflect what users saw months or years earlier. Prepared exhibits built from data collected closer to the events, with the collection method fully documented, are usually more reliable. Where historical results cannot be recovered, it is better to say so plainly than to present a present-day search as if it recreated the past.

Questions counsel can ask early

1. Which automated system produced the result at issue: search, autocomplete, feed or ads?
2. What data from the matter shows how that system behaved?
3. Is there an ordinary explanation that fits the data as well as the alleged one?
4. What can and cannot be known from outside the platform?

Personalisation and why two people see different results

A recurring source of confusion is personalisation. Two people searching the same phrase, or scrolling the same platform, may see different results because of location, device, language, account history and timing. Litigants sometimes present a single screenshot as if it showed what everyone saw. An expert can explain why that assumption is unsafe and, where possible, gather results under controlled conditions that reduce the influence of personal history.

Controlled collection usually means documenting the location, device, browser state and time for every capture, repeating searches across several sessions, and recording any variation rather than selecting the most favourable example. The goal is a dataset that describes the range of what users were likely to see, not a single image chosen because it supports one side.

AI-generated answers add another layer

Search engines and assistants that generate written answers introduce a further source of variation, because the same question can produce differently worded responses on different occasions. Explaining that variability plainly, and recording multiple responses rather than one, helps a finder of fact understand why a single generated answer may not represent typical behaviour.