In her post for Relativity, Cristin Traylor points out what you may have missed in the Schulte v. LinkedIn Corp. case I covered earlier today.

In Cristin’s post (When the Court Doesn’t Blink: Schulte v. LinkedIn on AI for Review, available here) where California Magistrate Judge Laurel Beeler issued a discovery order addressing a dispute (among others) that directly concerned LinkedIn’s use of Relativity aiR for Review for their production.

The plaintiffs raised procedural objections to how LinkedIn was using aiR. They challenged LinkedIn’s decision to apply search strings to cull documents before running the data set through aiR, and they moved to compel additional disclosures about aiR’s performance metrics. The court denied both motions.

As Cristin notes: “What the plaintiffs did not do – and what neither party nor the court treated as remotely controversial – was challenge LinkedIn’s use of aiR to make final responsiveness calls.”

Regarding the use of search strings to pre-cull the collection, Cristin points out: “The court’s reasoning on the search-string question is straightforward and grounded in established case law. Using keyword search to create a target population before applying a technology-assisted review workflow is not a new practice, and courts have consistently found it satisfies the reasonableness and proportionality standards of Rules 26(b) and 34(b)(2). Judge Beeler applied the same analysis here.”

Cristin also points out that, in her ruling, Judge Beeler noted: “Paragraph 5(a) of the [ESI] Order requires the producing party to ‘disclose to the receiving party if they intend to use Technology Assisted Review (“TAR”) to filter out non-responsive documents.’” I’ll add the following sentence to that quote from the ruling: “On May 15, 2026, LinkedIn disclosed to the plaintiffs that it would use Relativity aiR — a form of Technology Assisted Review — to filter out non-responsive documents.”

A lot of people define technology assisted review narrowly as the traditional TAR workflows (e.g. TAR 1.0 TAR 2.0/CAL, etc.). But Judge Beeler found that the use of generative AI in review is a form of TAR.  So, not only did the plaintiffs or the court not challenge the use of aiR to make final responsiveness calls, Judge Beeler equated the use of aiR with the use of TAR in terms of appropriate workflows.

Cristin Traylor points out what you may have missed in the ruling: “Generative AI for legal data review is actively in use, and the simple fact of that use is uncontested. The questions courts are being asked to resolve are operational” and they are “the same questions practitioners have been answering about TAR for more than a decade”.

Couldn’t agree more. So, what are a few practical takeaways worth carrying into your next review project? Find out here, it’s only one click! Don’t miss it! 😊

So, what do you think? Do you feel that this decision cements acceptance of GenAI review in the courts? Please share any comments you might have or if you’d like to know more about a particular topic.

Image created using DALL-E 3, using the term “a human supervisor checking the work of robot workers”.

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