Shark week, er, eDiscovery Case Week, continues today! In Federal Trade Commission v. Uber Technologies, Inc., No. 25-cv-03477 (N.D. Cal. July 2, 2026), California Magistrate Judge Thomas S. Hixson granted the FTC’s request for Uber to produce a random sample of the training documents, ordering Uber to “produce a random sample of 300 non-privileged, non-responsive documents from the initial seed set”. However, he denied the FTC’s request to change the recall rate for Uber’s TAR protocol from 75% to 85%.

Case Background and Judge’s Ruling

In this case, the FTC said that Uber made its first custodial document production of about 18,000 documents and represented that its production is 25% complete. The FTC extrapolated from this that Uber would likely produce around 72,000 custodial documents and stated “[t]his is a shockingly low figure,” determining there was a problem with Uber’s TAR protocol. Uber said the FTC’s math was wrong because its 25% estimate excluded Slack materials, and that the more likely end result was a production of around 156,000 documents.

The FTC made two requests. First, it asked the Court to change the recall rate for Uber’s TAR protocol from 75% to 85%. Second, it asked the Court to order Uber to produce a random sample of the training documents marked non-responsive.

Regarding the first request, Judge Hixson stated, in denying the request: “the Court has given Uber a July 13, 2026 deadline to complete its custodial document production…Changing the recall rate 11 days before the production deadline is not practical or feasible.”

However, Judge Hixson stated that the second request “has merit”. Continuing, he said: “Sometimes when parties use TAR they exchange training sets, so each side can see what the other is calling responsive. And then when they’ve completed production, they exchange validation information to show they’ve met the agreed upon metrics. When that happens, the parties can then bring to the Court any disputes about responsiveness. Here, that kind of transparency does not exist. Uber’s document reviewers train the TAR model with their responsiveness calls, but the FTC does not see what those calls are. Allowing the FTC to review a random sample of the training documents that Uber’s document reviewers marked as non-responsive would let the FTC see if the model is being trained improperly, and if it is, the Court could order Uber to retrain it.”

Both parties cited Winfield v. City of New York, 2017 WL 5664852 (S.D.N.Y. Nov. 27, 2017). The FTC cited it in support of its request for a random sample of non-responsive training documents so it could determine if the TAR model has been appropriately trained. Uber cited the same case for the proposition that courts have held that documents used to train a TAR model are work product.

Noting that “Winfield similarly dealt with an objection to how the defendant was coding documents as responsive or non-responsive for purposes of training its TAR model” and that the court in that case “ordered the defendant ‘to provide to Plaintiffs a sample of 300 non-privileged documents’”, Judge Hixson stated: “Thus, the case cited by Uber ordered the relief the FTC seeks here. Winfield does not stand for the proposition that the training documents coded as non-responsive are work product, as it ordered random samples of them produced.”

He added: “Also, think about Uber’s work product argument for a moment. If the non-responsive documents are work product because producing them would reveal counsel’s thought processes, then the responsive documents would also be work product for the same reason. Under Uber’s reasoning, every document review should result in no documents being produced. That doesn’t make any sense.”

Noting that “the parties disagree on whether Uber’s 10% responsiveness rate means the search terms are broad (Uber’s view) or Uber’s responsiveness calls are too narrow (FTC’s view)”, Judge Hixson stated: “There is no real way to answer that question without data. Accordingly, it is appropriate to require Uber to produce a random sample of 300 documents that were marked as non-responsive to allow the FTC to test Uber’s responsiveness calls.”

Judge Hixson also referenced a discussion at the hearing “about the denominator from which the 300 documents should be drawn”, where Uber suggested it should be the output of the TAR model rather than the coding decisions that were the input, while the FTC requested that the random sample be taken from the initial seed set that was first used to train the model. The FTC explained that it understands that there were later coding decisions made to further train the model, but it wants to see the initial responsiveness calls to see if there were problems right from the beginning.

Judge Hixson stated: “That sounds like a good idea. The FTC has doubted Uber’s 10% responsiveness rate from the beginning, and that rate has held fairly constant, suggesting that the human reviewers have likely been consistent in their responsiveness calls. An additional benefit to sampling the initial seed set is speed. If the sample were drawn from the final output of the model, or the final set of training documents, it could not be drawn until after custodial document production is completed by July 13. By contrast, Uber represents it can produce a random sample drawn from the initial training set by July 10.” So, Judge Hixson granted ordered Uber to produce a random sample of the training documents.

So, what do you think? Do you agree with the Court’s ruling to order Uber to produce a random sample of the training documents? Please share any comments you might have or if you’d like to know more about a particular topic.

Case opinion link courtesy of Minerva26, an Affinity partner of eDiscovery Today.

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