AI washing checker for public AI product claims
If you searched for an AI washing checker, use this buyer-side workflow to turn public AI product wording into evidence requests, buyer questions, and safer wording for procurement and due diligence. Start with the checker when you have the exact vendor sentence; use this page when you need examples of high-burden claim patterns first.
Use a public URL when you want readable page content checked. Paste one exact sentence when you need a claim-level receipt. The output is an evidence-burden note, not a vendor verdict. It does not label companies as fake, fraudulent, compliant, or safe to buy.
What the check looks at
An AI washing claim review looks at the evidence burden a public claim carries: whether the wording names enough scope, limits, and supporting conditions for a buyer to rely on it.
The review asks: does the wording create a higher evidence expectation than the vendor has disclosed? If so, that is an evidence signal, not a verdict.
Every review returns an evidence-burden note, evidence needed, buyer questions, and a wording boundary — the scope and conditions a claim would need to carry lower evidence risk.
Three common claim patterns and what they need
"Our AI is 98% accurate for any document type."
Accuracy / Performance
Numeric accuracy without visible test scope, content type, or error-rate breakdown.
Test set design, content types covered, model version tested, false-positive and false-negative rates, retest cadence.
Does the 98% figure hold for the document types in your workflow?
"Fully automated — no human review needed."
Automation / Replacement
Full-automation claim with no stated human review boundary, escalation path, or failure handling.
Task scope, human oversight steps, escalation triggers, failure mode documentation.
Which tasks still route to a human reviewer, and what triggers that escalation?
"AI-powered compliance for HIPAA and GDPR."
Compliance / Safety
Compliance promise tied to a named regulatory framework without stated scope, audit, or customer responsibility.
Framework scope, covered controls, audit or assessment method, customer responsibilities, and known exclusions.
Does this cover your deployment configuration, data flows, and sub-processor chain?
Why evidence burden matters for buyers
A vendor claim carries a higher evidence burden the more it promises — in scope, accuracy, compliance, ROI, or exclusivity. When evidence is not disclosed, the buyer carries that gap.
- Accuracy or performance claims need benchmark design, sample size, error rates, and scope limits. A single number without those is not enough to evaluate reliability in your workflow.
- Full-automation claims need a human review boundary, escalation path, and failure mode. Without those, the buyer cannot assess operational risk.
- Compliance or safety claims need a framework scope, audit method, and statement of customer responsibility. A claim that something is "fully HIPAA compliant" tells a buyer nothing about what controls are in place.
- Vague AI-powered descriptors like "AI-native" or "agentic AI" need a description of what AI is actually doing and what its boundaries are.
None of this is a legal determination. The evidence-burden label describes wording risk and support needed only — not legality, compliance status, or purchase safety.
How to use the checker
- Paste the vendor's public product page URL when the page is readable, or paste one specific AI claim sentence directly.
- The tool extracts AI-related claims and returns an evidence-burden note, evidence needed, buyer questions, and a wording boundary for each claim.
- Review the Claim Receipt — copy it, share it, or bring it into a vendor evaluation or due-diligence document.
The checker works on public product pages, landing pages, marketing copy, and case study excerpts. It does not support PDFs, private pages, or batch scanning.
AI claims checker questions
- Is this the main Check AI Claims product?
- No. This page uses the AI washing search term as an entry point. The product is Check AI Claims: a buyer-side review workflow for public AI product wording that returns evidence requests, buyer questions, and safer wording boundaries.
- Can I use it for an AI product claim before buying?
- Yes. Paste the vendor page URL or the exact claim text. The checker returns evidence needed and buyer questions that can be used in vendor evaluation or due-diligence notes. It does not approve or reject the vendor.
- Is this an AI compliance checker?
- No. It does not return a pass/fail or vendor verdict. If the wording mentions HIPAA, GDPR, SOC 2, ROI, accuracy, automation, or benchmark results, the checker asks what evidence should support that public claim.
- What should I paste into the checker?
- Use a public product page URL, a landing-page section, or the exact sentence you want to review. The clearest inputs are claims such as a percentage accuracy number, a fully automated promise, a compliance phrase, a cost-saving claim, or a first/only/best statement.
Sources
- ASA/CAP AI as a marketing term report Guidance on vague AI descriptor wording and evidence expectations for marketers.
- FTC AI claims enforcement — Content at Scale AI detector accuracy claim and evidence-retention case.
- FTC Business Guidance on AI FTC guidance on keeping AI claims substantiated.
What this tool does not do
- It does not decide whether a company or product is good or bad.
- It does not provide legal, compliance, investment, or procurement advice.
- It does not rank vendors or publish accusation lists.
- It does not make purchase decisions for a buyer.