AI claim substantiation: what evidence should support the wording?
Last reviewed June 5, 2026
AI claim substantiation starts with one specific statement: the accuracy number, compliance promise, bias claim, automation claim, ROI result, benchmark score, or first-of-kind wording a vendor publishes. Use this evidence checklist to map the claim to the support a buyer should ask for before relying on the wording.
Or load a documented sample into the checker
Fastest path: copy one exact vendor sentence that matches this pattern, then open the checker. Add the public URL only if you want readable page context recorded alongside the wording. The result is an evidence-burden note you can reuse in vendor follow-up or internal review, not a verdict. Not sure what a result looks like? See a sample receipt.
What to verify before you rely on the claim
- The exact claim text and the date it appeared.
- The evidence type that matches the claim type: benchmark, audit, subgroup test, customer sample, or workflow documentation.
- Scope limits: content type, language, model version, jurisdiction, user type, or deployment setting.
Sources behind AI claim substantiation
- · September 25, 2024
Source for AI-powered business-opportunity and income claim evidence.
- · April 22, 2025
Source for AI-powered accessibility compliance claim evidence.
- · December 3, 2024
Source for facial recognition accuracy, bias, training-data, and anti-spoofing claim evidence.
Documented AI claim substantiation examples
"stores producing five-figure monthly income by the second year"
ROI / Outcome- Source and date
- FTC Operation AI Comply announcement · September 25, 2024
- Evidence signal
- Projected monthly income wording tied to AI-powered ecommerce tools without visible customer result distribution.
- Evidence gap
- The buyer needs the customer sample, total cost basis, median outcome, loss rate, time period, and evidence that the AI component caused the result.
- Buyer question
- For the five-figure monthly income claim, what median customer result and loss-rate data supports the outcome after all costs?
"make any website compliant with WCAG"
Compliance / Safety- Source and date
- FTC accessiBe · April 22, 2025
- Evidence signal
- Broad automated compliance result with no visible scope limit.
- Evidence gap
- The buyer needs the WCAG version, issue categories tested, audit method, manual remediation boundary, and maintenance responsibility.
- Buyer question
- For the any website compliant claim, which WCAG criteria are automated and which still require manual review?
"system can't be tricked by a photo or video image"
Accuracy / Performance- Source and date
- FTC IntelliVision · December 3, 2024
- Evidence signal
- Anti-spoofing wording with no visible attack-type or test-method boundary.
- Evidence gap
- The buyer needs spoofing attack types tested, field conditions, success rate, model version, update cadence, and known limitations.
- Buyer question
- For the can't-be-tricked claim, which photo, video, mask, replay, or presentation attacks were tested?
"no bias in recognition across all demographics"
Accuracy / Performance- Source and date
- FTC IntelliVision press release · December 3, 2024
- Evidence signal
- Zero-bias claim applied to all demographic groups without visible subgroup error rates, test population, or independent audit.
- Evidence gap
- The FTC found IntelliVision made demographic accuracy claims without the subgroup-level data needed to support them. A buyer needs false acceptance and false rejection rates by demographic group, the dataset used for bias testing, the performance gap between the best- and worst-performing subgroups, and whether testing was conducted internally or by an independent lab.
- Buyer question
- For the no-bias claim, which demographic groups were tested, what were the false acceptance and false rejection rates by group, and who conducted the bias evaluation?
Evidence map for AI claim substantiation
| Claim pattern | Evidence needed | Buyer question |
|---|---|---|
| Accuracy or performance number | Benchmark source, sample size, input categories, model versions, error rates, and date tested. | Does the test match the same content, user, and workflow where we would rely on the product? |
| Compliance or safety result | Standard version, audit scope, issue coverage, exclusions, remediation steps, and maintenance boundary. | Which part of the claimed compliance result is produced by AI and which part requires human review? |
| Bias-free, fair, or safe output claim | Subgroup metrics, test population, failure examples, review process, and update cadence. | What failure rate appears for each subgroup or high-risk input category? |
| Automation or replacement claim | Task boundary, escalation path, human review point, unsupported use cases, and quality checks. | Where does the automated workflow stop before a qualified person reviews or acts? |
| AI product claim evidence checklist | Exact words, source URL, publication date, claim type, matching evidence type, scope limits, and the vendor owner responsible for the support. | Can the vendor produce the source, date, method, and limitation that support this exact AI product claim? |
Evidence buyers need for AI claim substantiation
- The exact claim text and the date it appeared.
- The evidence type that matches the claim type: benchmark, audit, subgroup test, customer sample, or workflow documentation.
- Scope limits: content type, language, model version, jurisdiction, user type, or deployment setting.
- Known exclusions, failure states, and human review boundaries.
- A wording boundary that names the tested scope instead of broad wording.
Buyer questions for AI claim substantiation
- For this AI claim, what evidence existed before the wording was published?
- Does the evidence test the same use case, customer group, and input type named or implied by the claim?
- What metric would change the claim's evidence burden: accuracy rate, bias result, compliance promise, automation scope, or first-of-kind wording?
- Can the vendor provide the source, date, method, and limitations behind the claim in writing?
- What evidence is needed for this AI claim before a buyer can rely on it?
- Can the vendor explain whether the product used AI in a way that supports the public wording, or only in a narrower workflow step?
Safer wording for AI claim substantiation
- Reported X% accuracy on a named benchmark covering specified content types and model versions.
- Supports selected WCAG remediation tasks; manual review and ongoing maintenance remain necessary.
- Tested across named demographic groups in a specified setting, with subgroup results available on request.
- Automates defined workflow steps and routes exceptions to qualified human review.
AI claim substantiation questions
- What evidence is needed for an AI claim?
- Start with the exact claim type. Accuracy claims need test scope, input categories, model version, and error rates. Compliance or safety claims need the standard, audit scope, exclusions, and customer responsibilities. ROI claims need baseline, sample, cost basis, and measurement method. Automation claims need task boundary, human review, and failure handling.
- Can an AI claim be misleading even if the product really uses AI?
- Yes. A product may use AI in one workflow step while the public wording implies broader accuracy, full automation, compliance coverage, or business outcomes. This page does not make a legal conclusion; it identifies the evidence a buyer should request for the exact wording.
- Is AI claim substantiation a legal review?
- No. This guide maps public wording to evidence questions for buyers. It does not make pass/fail decisions, compliance assessments, purchase recommendations, or vendor verdicts.