//The Standard · v1.0
The Evidence-Grade AI Standard
A framework for producing AI-assisted investigative findings that survive a courtroom challenge. Eight pillars and a checklist, from a licensed investigator who builds the systems and validates the work.
The AI is never the witness. A qualified human is.
AI accelerates the work. A licensed, credentialed examiner sources it, verifies it, and stands behind every finding. Evidence-Grade AI is AI used that way, and documented so it can be proven. This is a professional framework and general information, not legal advice.
//Why it exists
Why this Standard exists
AI is entering investigations and courtrooms faster than the rules governing it. Most AI used in investigative and legal work today is not built to be defended: it hallucinates, it relies on tools no one has validated, it leaves no record of how a result was produced, and it is used without disclosure or independent verification. When that work is challenged, it fails, and the case fails with it.
Proposed Federal Rule of Evidence 707 signals where this is going: machine-generated evidence offered without a sponsoring expert would have to meet the same reliability standard the courts already apply to expert testimony under Rule 702 and Daubert. The rule itself is still being written and its timeline is unsettled, but that is beside the point, because it only extends a standard that is already the law. Someone has to be able to show the machine's output is reliable. This Standard is that showing, written down and made repeatable.
//The framework
The eight pillars
Each pillar states a principle, ties it to the reliability standard courts apply, and gives the practical requirement.
01
Competent Human Authority
The work is directed and owned by a qualified human, appropriately licensed or credentialed for the matter, who is accountable for every finding.
A named, qualified examiner supervises the workflow and signs off on the result. AI does not make judgment calls, legal determinations, or final findings on its own.
02· reliability: sufficient facts and data
Sufficient, Sourced Inputs
A finding rests on sufficient, identified, and lawfully obtained data, not on the model's training or its guesses.
Every input the finding depends on is identified and preserved. Data is lawfully sourced under the same licensing, privacy, and evidence rules that govern the work without AI. Prompts are captured, because in an AI workflow the prompt is part of the data.
03· reliability: reliable principles and methods
Reliable Method
The AI-assisted method is documented, explainable, and repeatable, not a black box.
The method is written down so another qualified examiner could follow it. The specific system and its version are recorded. Where reliability is not self-evident, it is supported by testing, validation, or accepted practice.
04· reliability: reliable application to the facts
Reliable Application
The method was actually applied correctly to the specific facts of this matter.
Outputs are checked against the underlying sources for this case, not accepted because the method is generally sound. Errors and limitations are noted rather than smoothed over.
05
The Human Verification Gate
No AI output becomes a finding until a qualified human has independently verified it against source evidence.
Verification is a distinct, documented step, not an assumption. Unverified AI output is treated as a lead, never as a conclusion. This is the single discipline that separates Evidence-Grade AI from everything else.
06
Chain of Custody and Provenance
The inputs, the process, and the outputs are preserved with a defensible record.
The record answers, for any finding: what data was used, what system and version produced the output, what steps were involved, who verified it, and when. Evidence and its metadata are preserved so the trail survives challenge.
07
Disclosure
AI's role is documented and disclosed where required, and never hidden or overstated.
The work product states how AI was used and what a human did. Claims are calibrated to what the method can support. Acceleration is not presented as human analysis, and a probability is not presented as a certainty.
08
Reproducibility and Audit
An independent examiner, given the record, could follow it and reach the same result.
The workflow is built to be audited. The Evidence-Grade AI Audit, and in time the credential, exist to validate that a firm's workflow and its people meet this Standard as models and rules change.
//The gate
The Evidence-Grade AI Checklist
Run this against an AI-assisted workflow, or against a specific finding, before it leaves the building. A “no” on any line is a defensibility gap to close.
- A named, qualified, appropriately licensed human directs the work and owns the finding.
- The AI made no judgment call, legal determination, or final finding on its own.
- Every input the finding depends on is identified and preserved.
- All data was lawfully sourced under the rules that govern the work without AI.
- The prompts and instructions given to the AI are captured.
- The method is documented well enough for another examiner to follow.
- The specific system and its version are recorded.
- Every AI output the finding relies on was independently verified against source evidence.
- Unverified AI output was treated as a lead, not a conclusion.
- There is a record of what data, what system, what steps, who verified, and when.
- The work product discloses how AI was used and what the human did.
- No claim overstates what the method can support.
- An independent examiner could reproduce the result from the record.
//Using it
How to use this Standard
Investigators & examiners
Adopt the pillars as your working discipline and the checklist as your pre-delivery gate.
Law firms & insurers
Require Evidence-Grade AI of the investigators and vendors whose AI-assisted work you rely on, and validate it before you build a case or a claim on it.
Vendors & builders
Design your systems so the record the Standard requires is produced automatically.
The Evidence-Grade AI Audit validates a firm’s workflow against this Standard. See also how to make your AI investigation survive Rule 707. The Standard will be versioned as the technology and the rules of evidence evolve.