Raising AI Review at the Rule 26(f) Conference

The Rule 26(f) conference is where AI review terms are cheapest to settle.

AI-assisted review belongs on the Rule 26(f) agenda, and you should be the one who raises it. Before the conference, draft the one page you would want a judge to read later, and circulate it with the agenda rather than after the first objection. Courts and opposing parties now expect proof that an AI review workflow is well designed and that its output is accurate enough for a fair adjudication at trial. Silence does not spare you that scrutiny. It only postpones it.

The meet-and-confer has always been where format fights are settled cheaply. In a 2022 r/ediscovery thread on native productions, one practitioner recalled parties quarrelling over whether headers or footers had existed in the native file at all, which is why, in that commenter's account, Bates-stamped images remained the "best case." The lesson travels. A dispute about what a document is, or how it was coded, costs little when agreed in advance and a great deal when discovered at deposition.

What follows is the disclosure I would make, the reply I would give to each objection, and the validation terms that turn an objection into a protocol clause, beginning with the words you say first.

What should you disclose about AI review at the Rule 26(f) conference?

Disclose the method, the human sign-off and the validation plan, measured like TAR, backed by an audit trail and chain of custody that hold up if opposing counsel challenges it.

1. Name the method. AI-assisted first-pass review, with an attorney making every final responsiveness and privilege call.
2. State the yardstick. Recall and precision, the same measures already used for technology-assisted review (TAR).
3. Offer the test. A random sample drawn from documents the system classified as non-responsive, reviewed by an attorney before production is certified complete.

In 2024, a practitioner in the r/ediscovery community reported that the lowest scores they had ever seen accepted on a TAR 1.0 workflow were 70% recall and 50% precision. That is one person's account, not a court threshold. Yet it shows where the conversation lives: in two numbers. Recall is the share of truly responsive documents the review actually found; precision is the share of flagged documents that were truly responsive. Another commenter in the same 2024 thread observed that precision and recall "can be calculated for any methods," whether generative AI, TAR 2.0, managed review or search terms.

The common assumption is that generative AI review needs a new legal standard before anyone may use it. Before you accept it, look at what the other side has already signed. Ask opposing counsel which measure their last ESI protocol used. If that protocol named recall, propose the same term for yours. Ahmadia and four co-authors, writing in the Journal of Technology Law & Policy in 2026, set out random and stratified sampling, confidence intervals and margins of error as the tools for testing whether an AI workflow meets the Federal Rules of Civil Procedure, and they conclude that courts and opposing parties "rightly demand heightened verification that the workflow is well designed." Advisory commentary adds a practical warning: nobody reviews the discard pile, so a recall failure leaves no trace at the moment it occurs. A comparison of 4 sources shows the same pattern. The measurement travels across methods, and the burden of describing it falls on the producing party.

To this end, I would put something close to the following on the table, in writing, before the conference begins:

Producing Party intends to use AI-assisted review to prioritize and classify documents for responsiveness. An attorney will review and approve every production and withholding decision. Producing Party will validate the review by drawing a random sample from documents classified as non-responsive, having an attorney review that sample blind, and reporting estimated recall and precision before certifying that production is complete.

Nothing in that paragraph reveals a prompt, a model setting or attorney work product. It describes a process, which is what the other side is entitled to evaluate. The record behind the words matters as much as the words. Our platform was built on that premise: immutable originals with content hashing, append-only audit trails, documented chain of custody and a fail-closed privilege gate on production, the last of which is described in detail in our piece on designing a privilege gate for privileged ESI. A process stated this plainly is hard to attack. Which leaves the harder moment, of course: the other side hears every word of it and still says no.

What do you say when opposing counsel objects to AI review?

Answer each objection separately: accuracy with a recall test, hallucination with outputs linked to the exact source exhibit, privilege with data that never leaves your control.

Objections to AI review arrive in three forms, and they are seldom as mixed as they first sound. Sorting them is the first act of persuasion. An accuracy objection asks whether the method finds enough; a hallucination objection asks whether it invents; a privilege objection asks where the documents go. Each deserves its own reply.

When the objection is accuracy

What you might hear: the system will miss responsive documents.

What I would say:

We share the concern, which is why we propose to measure it rather than assume it. We will sample the documents the system set aside, have an attorney review that sample, and report estimated recall. We will accept the same measurement for any review method either side uses, including human review.

The last sentence carries the weight. Vendor-sponsored commentary from 2026 claims that courts generally accept recall of at least 85% in document review, a benchmark human reviewers are rarely measured against at all. The asymmetry it describes is real enough to use. The research section below sets out what a court would actually measure.

When the objection is hallucination

What you might hear: generative AI fabricates facts.

The concern is fair. Professional-services commentary warns that generative AI can create plausible connections out of unrelated facts, and that outputs acted on without human review can distort relevance and privilege calls. The reply, then, is not denial but architecture.

No AI output is acted on without attorney review. Every classification and every summary links back to the source document, so the attorney signs off on the document itself, not on the machine's description of it.

In our platform, every answer traces to the exact exhibit it came from, so an attorney can verify it in one click before filing, which is the structural answer to the sanctions risk that Mata v. Avianca made notorious. The case for source-linked review as the new defensibility bar is set out at greater length elsewhere on this site.

Verification, moreover, runs in both directions. In a ruling discussed on r/Lawyertalk in 2026, a court declined to sanction an attorney who had failed to catch an opponent's fictional citations, yet reminded counsel on both sides of their obligation to complete "a basic cite-check of the cases cited by the other side." The court added that it "should not be left as the last line of defense against citations to fictional cases in briefs filed with the court." Counsel who object to your method are expected to check your outputs anyway. Source links make that check fast.

When the objection is privilege

What you might hear: loading privileged ESI into an AI tool risks waiver, or trains someone else's model.

Processing runs in a single-tenant environment or inside our own cloud account under our own keys, with no vendor retention and no model training. We are prepared to write those terms into the protective order.

Our architecture is designed so that privileged evidence never leaves the firm's control: processing runs single-tenant or inside the client's own AWS account under its own keys, with no vendor retention and no model training. Stated that way, the objection loses its object. What remains is the question every objection eventually becomes: prove it on this matter, with these documents, before the protocol is signed.

How do you prove AI review is proportional on your own matter?

Before the conference, run the tool on your own messy collection with a hard question and watch it read, code and cite your evidence.

1. Use the real collection. Load the matter's own documents, not a sample set, and ask a question the case turns on.
2. Check the answers. Open the cited source behind every answer and note each one that is wrong or incomplete.
3. Record the cost. Write down the cost per document and the hours spent, so the burden argument rests on this matter.

Scripts answer objections in the room. Evidence ends them before anyone enters it. Our demonstration rests on exactly this premise: bring a messy collection and a hard question, then watch the system read, code and cite your own evidence rather than reading a case study about someone else's. A hard question matters more than a large one. If the tool can find the email that decides the case, the conversation about recall becomes concrete; if it cannot, you would rather learn that now than at a deposition.

The cost record carries equal weight, because proportionality is argued in dollars. In 2025, ComplexDiscovery reported that review had fallen from 73% of eDiscovery task spending in 2012 to 64% in 2024, and projected 52% by 2029. Its Winter 2025 pricing survey, run with EDRM, found GenAI-assisted review most often priced at $0.26 to $0.50 per document, while nearly one-third of respondents reported no familiarity with GenAI-assisted review at all. The market was moving faster than its own understanding. Opposing counsel may share that unfamiliarity. A number from your own matter cures it.

Our economics are demonstrable, not asserted: AI-assisted review runs at cents per document, against the dollars per document and roughly $19K/GB of manual review, a delta the buyer can see on their own matter. For a solo or small firm on a single messy matter, that difference decides whether a defensible review is affordable at all. The platform was designed for precisely that segment, with enterprise-grade defensibility at small-matter economics and no administrator required to run it.

The burden, moreover, is felt on both sides of the table. One eDiscovery software vendor's 2026 survey found that nearly 6 out of 10 legal professionals name document management, review and reduction as their top pain point. That symmetry is useful at the conference. A party who objects to the efficient method must explain why the slower and costlier one is proportional, and that explanation is seldom as easy to give as the objection was to make.

It is worth testing, too, whether a planted document can fool your AI review tool, since a careful opponent may raise exactly that possibility. What you learn in that hour of testing becomes the protocol you propose, and the protocol is where every one of these numbers must finally be written down.

What will the next meet-and-confer about AI review look like?

Expect less argument over whether AI may be used and more negotiation over how its output is validated, which coding fields are exchanged, and who signs off.

The receiving side is already preparing. In a 2025 r/ediscovery discussion, a litigator facing an opposing production of 30,000+ documents answering 50 Requests for Production wanted generative AI to map each document to a request and expose the gaps. A commenter noted that the tool's issue analysis maxes out at ten, so the requests would have to be clustered. Another offered the better advice: request issue coding in the ESI protocol, and it arrives in the .dat load file. Negotiated at the conference, the dispute never ripens.

I will concede a trade-off. Issue coding delivered in a load file shifts mapping labor onto the producing party, and a small firm on that side may fairly resist carrying it. Agreeing the burden in advance at least lets you price it.

So bring the disclosure language, bring the validation terms, and assume the other side will run its own AI across your production.