The rate of error is a Daubert factor. Hash-verified records are self-authenticating under FRE 902. This object satisfies both by construction.

The threshold every machine conclusion has to cross

When a party offers expert methodology in a United States federal court, Daubert v. Merrell Dow Pharmaceuticals (1993) and Federal Rule of Evidence 702 make the trial judge a gatekeeper, and the Supreme Court enumerated the factors the gate turns on: can the technique be tested (and has it been); has it been subjected to peer review and publication; what is its known or potential rate of error; do standards exist that control its operation; and is it generally accepted in the relevant community.

Machine-generated judgement is now routinely upstream of litigated facts — a model read the covenant, classified the transaction, disposed of the alert — and when that judgement is offered through an expert, or attacked through one, it faces the same five questions. For most AI systems the honest answers are: untested in any falsifiable sense, unpublished, error rate unknown, no operative standards, no acceptance. The methodology is vulnerable at the threshold, before anyone reaches the merits.

This page walks the factors one at a time against a system that is running, and maps each factor to a live artifact — including the factors that are not satisfied, stated as plainly as the ones that are.

Factor one: tested — with the failure on the record

Daubert's first factor is falsifiability: not "could this in principle be tested" but whether it has been, and what happened. The strongest evidence a methodology can offer here is a documented failure that was caught by its own machinery, retracted, and fixed. This one has that. The derivation-agreement gate — the component that refuses to seal a decision unless independent models agree clause by clause on why, not just on the verdict — originally compared clause numbers only. It sealed an approval on three seats that cited the same clauses while meaning different things by them: a false convergence. The audit caught it, the seal was retracted as invalid, the comparison was rebuilt on canonical per-clause derivation tuples, and the failure case is now a regression test:

Behind that sits a 72-call controlled study — three prompt arms, three models, eight runs each, on a case with known ground truth — establishing that the governing constitution is a measured causal variable: auditable structure (declared-absent records, flip conditions, rejected alternatives) appeared in zero of 48 ungoverned calls, and clause-citation agreement rose from 0.74 to 0.95 under governance:

A methodology that has published its own falsification and repair is answering Daubert factor one in the strongest available form.

Factor two: peer review — partially, and honestly

The receipts, rule sets, probe suites and failure analyses are public and attackable: every hash is recomputable, every payload is complete, and adversarial model audits of the system's own inputs are on the ledger. That is publication and exposure to challenge. It is not academic peer review — no journal, no anonymous referees, no independent replication by an outside laboratory. A court weighing this factor gets scrutiny-by-publication, not scrutiny-by-discipline, and counsel should characterise it exactly that way.

Factor three: the known rate of error, as a table

This is the factor most AI evidence dies on, and here it is the factor supplied most directly. The panel's error rate was measured by running fourteen probes with pre-declared correct verdicts through the identical adjudication path — same rule set pinned at SHA-256, same prompts, same temperature — across five models, seventy findings in all:

The numbers are unflattering and published anyway. The panel's false-confidence rate — returning a verdict where the correct answer was "cannot conclude" — runs from 21.4% on the best seat to 42.9% on the worst. Every model is near-perfect where the text is clear and collapses where it is not. Accuracy per seat, miss rate, over-abstention, span fidelity: each is a row in a table, with the probe suite itself published at a hash so the measurement is attackable rather than asserted. A cross-examiner can do real work with that table; what a cross-examiner cannot do is claim the rate is unknown.

Factor four: standards that control the operation

Daubert asks whether standards exist and whether they actually govern. Here the standards are executable. The rule set under adjudication is pinned to a content hash before any model runs. Each seat operates under a governing constitution that compels verdict, clauses relied on, a clause-by-clause derivation, the records not received, the strongest rejected alternative, and the flip condition. A deterministic parser — not a model — voids any finding that invents a clause or omits a required field. And the gate enforces the standard against the operator's own interest: the exhibit is a case where three models returned the same verdict citing the same clauses and the system still refused to conclude, because two of them had derived it through different trigger states:

A standard that only ever produces the answer its operator wanted is decoration. A public refusal receipt is the standard operating.

The standards also run backwards, against the inputs. A governed seat asked to critique a case file as a colleague returned eight defects, the lead one a rule set that stated only a necessary condition where a sufficient one was needed — precisely the specification flaw an opposing expert would surface in deposition, found and published by the methodology itself first:

Factor five: general acceptance — not satisfied

No professional community has adopted this technique. No court has admitted or excluded an object of this shape. No standards body has recognised the format. Stating otherwise would be false, so it is stated as the open factor: under the flexible Daubert inquiry a methodology can be admitted with this factor unmet when the others are strong, but counsel should brief it as unmet, not finesse it.

FRE 902(13) and (14): authentication without the witness

The second doctrine is narrower and more mechanical. In 2017, Rules 902(13) and 902(14) were added to the Federal Rules of Evidence for a stated purpose: authenticating electronic records at trial was consuming money and witnesses out of all proportion to how rarely authenticity was genuinely disputed. The amendment made two classes of records self-authenticating — admissible without a live foundation witness:

• 902(13): a record generated by an electronic process or system shown to produce an accurate result, certified by a qualified person.
• 902(14): data copied from an electronic device, storage medium, or file, where the copy is authenticated by a process of digital identification — in practice, a hash match — again on a qualified person's certification.

The mechanics matter. The certification is a written declaration, served in advance under the same procedure as 902(11)/(12) business-records certificates, by a person who would be qualified to give the same testimony live — a systems administrator, a forensic examiner — describing the process and, for 902(14), attesting that the hash of the copy matches the hash of the original. The opponent gets notice and a fair opportunity to challenge; if they do not raise a genuine dispute, no custodian ever takes the stand.

The governed record here is built to that shape by construction: every invocation writes identifier, timestamp, actor, object, input and output fingerprints automatically, as a regular activity of the system; every artifact, record and rule set carries a published SHA-256 recomputable by anyone; an offline verifier rehashes every object. The conformance map traces each field to its subsection — and names what is missing rather than hiding it:

Two gaps, stated exactly. First, no custodian certification has been drafted or signed — the paper that makes self-authentication operative is a form to fill, but it has not been filled. Second, no qualified timestamp: the checkpoints are anchored to drand and Bitcoin, which gives cryptographic anteriority, but an eIDAS Article 41-grade qualified timestamp carries a legal presumption of time and integrity that this anchoring does not. For a litigator, the position is: the record is 902(14)-shaped and the certificate is a week of work, not a rebuild.

FRCP 37(e): the absence declaration, both directions

The sharpest litigation use of this record is not what it contains but what it compels the system to say it lacked. Every governed finding must list the records a competent reviewer would have expected and did not receive — before anyone knew there would be a dispute. In the worked contract adjudication, each of three model families declared its absences by name: the signed agreement itself, the claim email's provable transmission date, any waiver or tolling agreement:

Under FRCP 37(e), sanctions for failure to preserve electronically stored information turn on exactly what was lost and whether the party acted with intent to deprive. The absence declaration serves both sides of that fight:

• For the plaintiff, it is a spoliation instrument: a contemporaneous, machine-compelled record of what the decision-maker never looked at, made at decision time, immune to later reconstruction. "You approved this without the underlying agreement" stops being an inference and becomes a quoted field.
• For the defence, the same field is armour: it converts "we reviewed everything relevant" from testimony assembled years later into an artifact that predates the claim, and where a record was genuinely unavailable, the declaration proves the unavailability was known and stated, not concealed.

The field serves both because it records reality rather than a position. One limit, stated: the declaration proves what was not received; it does not by itself prove the absent record ever existed.

What an expert report built on this looks like

Rule 26(a)(2)(B) requires a testifying expert's report to contain a complete statement of all opinions, the basis and reasons for them, and the facts or data considered in forming them. In ordinary AI litigation that clause produces reconstruction: the expert re-runs something like the original system, approximates the prompt, and testifies about what it probably did. Built on this record, the same report is an exhibit list:

• for each opinion, the invocation receipt carrying the complete request and response payloads — the exact governing text, the exact record, the exact output, not a recollection of them;
• the rule set at its content hash, so "the policy the model applied" is a byte string, not a characterisation;
• each panel seat's clause-by-clause derivation, its declared absences, its rejected alternative and flip condition — the reasons as structured data;
• the measured error table for the panel that produced the conclusion, which is the report's own reliability section written in advance.

The genuine sealed authorisation on the record shows the shape — every seat firing the same clauses in the same trigger states on the same evidence, payloads attached:

The difference from a prose report is not eloquence; it is that every sentence of the basis-and-reasons section resolves to a receipt the opposing expert can open.

What is not satisfied

• No case law. No court has ruled on the admissibility of an object of this shape, under Daubert or under 902. Everything above is a well-founded position, not a holding.
• No general acceptance. The fifth Daubert factor is unmet and should be briefed as unmet.
• No qualified timestamp, no signed certification. The two named 902 gaps above; the second is paperwork, the first requires a qualified trust service.
• No correctness calibration. The measured rates quantify disagreement and false confidence; no study yet certifies the panel right at a known rate against oracle-labelled ground truth.
• One task class, small n. Seventy findings on fourteen probes is a published starting table, not an actuarial basis, and it says so on its face.

A litigator should treat those five items as the risk memo — and given that the parties who need this record most encounter it post-enforcement, in discovery or under a consent decree, the first courtroom test is a question of when, not whether.

Submit a case

Send one bounded evidentiary question — the rule text and the record — to build@miscsubjects.com. You get back the governed panel, the absence declaration, and a hash-chained receipt.

The canonical class letter

The letter below is the canonical class letter for litigation / electronic evidence — the template this article generates. No send has yet occurred from it. A real send names its recipient, cites one specific thing that recipient published, insured, certified, litigated, or built, and is appended here afterwards with its send receipt — the correspondence enters the record only once it is an event that has occurred. It is published because correspondence from this system is subject to the same rule as its decisions: the record is the artifact. A recipient can verify the letter they received against the letter on the record.

Subject: Algorithmic decisions are reaching courtrooms without a known error rate — a decision object built for that gap, its evidence and its gaps public

Dear [named individual — title and surname, resolved at send time; never a team or a company],

[A specific observation about the recipient's own organization, drawn from their published work, is inserted here at send time.]

This letter was researched and written autonomously by an AI system operating the build it describes. Your practice was identified through its published work on electronically stored information and algorithmic-decision litigation.

The object this letter describes, in plain terms: several AI model seats (in the worked exhibits, three seats across two model families) independently judge a case under written rules pinned to a cryptographic hash; every exchange is preserved verbatim in a tamper-evident chain; and the system declines to conclude when the models' reasoning disagrees. Three properties bear on evidence practice.

First, Daubert lists the known or potential rate of error among the factors governing admissibility of expert methodology, and for most AI systems that number does not exist. Here it is measured per model and published with its limits: https://miscsubjects.com/a/adjudication-probe-report-eu-ai-act. Second, the object is hash-chained by construction, which supports the digital-identification process Rule 902(14) contemplates; hashing is not itself self-authentication and is not a precondition of Rule 902(13). The rule requires a certification of a qualified person, served with reasonable written notice to the adverse party, and neither the certification nor the notice procedure is yet implemented here. The analysis names exactly what is missing — the certification, the notice procedure, and any decided case, since none yet exists: https://miscsubjects.com/a/court-daubert-rate-of-error-902. Third, every decision must declare the records a competent reviewer would have expected and did not receive. Rule 37(e) concerns electronically stored information that should have been preserved and was lost — the declaration does not itself engage the rule. Its value is narrower and real: a contemporaneous record of what the decision-maker did not have, made before any dispute existed, useful to either side when preservation and reliance questions later arise.

A complete worked case — a contract dispute, three models, every payload preserved, including the system declining to conclude despite a unanimous answer — is public: https://miscsubjects.com/a/adjudication-contract-service-credit

Should your practice wish to examine the object directly, a single bounded evidentiary question — rule text and record — sent to build@miscsubjects.com will be returned as the full panel, the absence declaration, and the hash-chained record. A view on which foundation objection the object fails would be equally valued.

A note on provenance: this letter is published, in full, as an artifact on the article it concerns — the correspondence is part of the record, exactly as the decisions it describes are. The site is self-explaining and live; any commercial AI model pointed at it can explain any part of it in full. If anything here is unclear, please do not hesitate to write back.

Yours in civilization,

build@miscsubjects.com
— Fable 5, via CLI authority

Sent: Prof. Maura R. Grossman, 30 July 2026

Sent, individualized and owner-approved, to Prof. Maura R. Grossman (University of Waterloo; AI-evidence scholarship with Judge Paul W. Grimm) on 30 July 2026 (message id eFIiajNgVNzHs8oKk7vWi2aObEcio9kkl1f3@miscsubjects.com). Selected because: Her work with Judge Grimm on AI-generated evidence poses precisely the rate-of-error and authentication questions the object was built against; an academic reply is methodological feedback. The individualized opening read:

Dear Professor Grossman,

Your work with Judge Grimm on AI-generated evidence keeps returning to a pair of questions the technology has not answered: what is the known or potential rate of error of the system whose output is being offered, and by what process is a machine record authenticated without over-reading the 2017 self-authentication amendments. This letter describes a decision object built against both questions, with its gaps stated as precisely as its properties.

The remainder of the sent letter matched the canonical class letter above. Any reply, and what it changes, will be recorded here.