AI washing examples: claim patterns and evidence gaps
Last reviewed May 24, 2026
AI washing examples are useful only when they point to a specific claim pattern and the evidence needed to support it. This page uses official and high-evidence sources to show the wording pattern, not to compare companies.
Evidence buyers verify
- Exact claim text from the public page or official source.
- Claim type and the wording that raises the evidence burden.
- Specific evidence that would support the claim in the same use case.
Opens the checker for this claim type. Paste your vendor's exact wording there. Evidence questions only — not a blacklist or fraud detector. Not sure what a result looks like? See a sample receipt.
Sources this guide draws from
- · August 28, 2025
Detector accuracy claim pattern.
- · April 22, 2025
Automated accessibility compliance claim pattern.
- · February 11, 2025
Professional-replacement claim pattern.
- · March 18, 2024
AI use, forecasting, and first-of-kind claim patterns.
- · March 18, 2024
Source for client-data, predictive-investing, and AI-capability claim wording.
- · November 13, 2024
Official report context for vague AI marketing language.
Public claims with documented evidence gaps
"AI content detector can identify AI-generated text"
Accuracy / Performance- Source and date
- FTC Content at Scale AI · August 28, 2025
- Evidence signal
- Detector capability wording without enough visible test context.
- Evidence gap
- Benchmark scope, content categories, model coverage, and error rates.
- Buyer question
- For the AI content detector claim, what exact content types and model versions were tested?
"AI-powered web overlay for accessibility compliance"
Compliance / Safety- Source and date
- FTC accessiBe · April 22, 2025
- Evidence signal
- Broad automated compliance result.
- Evidence gap
- Standard version, audit method, manual remediation boundary, and ongoing maintenance.
- Buyer question
- For the any website compliant claim, what remains outside automation and requires human review?
"AI lawyer handles legal matters without professional review"
Automation / Replacement- Source and date
- FTC DoNotPay · February 11, 2025
- Evidence signal
- Professional-replacement language.
- Evidence gap
- Task scope, quality testing, professional review, and limits on legal-document use.
- Buyer question
- For the robot lawyer claim, which tasks were tested and which require professional review?
"turns your data into an unfair investing advantage"
Accuracy / Performance- Source and date
- SEC Delphia administrative order · March 18, 2024
- Evidence signal
- AI investment-advantage wording tied to client data and predictive capability.
- Evidence gap
- Data actually used, model capability at the claim date, decision boundary, and disclosure updates.
- Buyer question
- For the unfair investing advantage claim, what client data and model capability existed when the wording was live?
Match each claim pattern to the evidence buyers need
| Claim pattern | Evidence needed | Buyer question |
|---|---|---|
| Accuracy / Performance | Benchmark, test population, model list, sample size, and error rates. | Does the evidence match the exact content or workflow where we would rely on the claim? |
| Automation / Replacement | Human review boundary, failure handling, task limits, and escalation path. | Which steps are automated, and which steps still require qualified review? |
| Compliance / Safety | Applicable standard, audit method, known exclusions, and maintenance responsibilities. | Which standard is named, and what evidence shows coverage against that standard? |
| First / Only / Best | Comparison universe, time frame, source of comparison, and update process. | What happens if a competitor changes the comparison tomorrow? |
| Vague AI-powered | Specific model role, input, output, human step, and user-visible benefit. | If the word AI is removed, what concrete product capability remains? |
Evidence to request
- Exact claim text from the public page or official source.
- Claim type and the wording that raises the evidence burden.
- Specific evidence that would support the claim in the same use case.
- A buyer question that mentions the actual claim, not a generic AI question.
Questions to put in front of the vendor
- Which claim type is this: accuracy, automation, compliance, ROI, first/only/best, or vague AI-powered?
- What exact word raises the evidence burden: percent, any, first, fully automated, or AI-powered?
- What document, benchmark, audit, or disclosure would support this claim at the date it was published?
- How could the claim be rewritten with scope, date, and human review boundary included?
Wording boundaries to compare against
- Reported X% on a named benchmark, with limitations stated.
- Automates specified low-risk steps and routes exceptions to human review.
- Supports compliance work by identifying selected issues; final review and maintenance remain separate.
- Uses an AI model in named workflow steps instead of claiming broad AI transformation.
Frequently asked questions
- What is AI washing?
- AI washing is the practice of using AI-related terms—such as 'AI-powered', 'machine learning-driven', or 'intelligent'—in marketing without substantiating what the AI actually does, how it was tested, or what evidence supports the claimed result. Regulators including the FTC (Operation AI Comply, September 2024) and the SEC (Delphia and Global Predictions enforcement, March 2024) have taken action against companies whose AI claims were not backed by the evidence the wording implied.
- What makes an AI claim 'high risk' for buyers?
- An AI claim carries a higher evidence burden when it uses absolute or comparative language (zero, perfect, always, highest), professional-equivalence wording (like a lawyer, as good as a doctor), compliance certification language (HIPAA-compliant, GDPR-ready, fully ADA compliant), or quantified outcomes (saves 40% of costs, 99% accuracy) without naming the test conditions, populations, and failure modes that define the figure.
- How is AI washing different from ordinary marketing exaggeration?
- Ordinary puffery—words like 'leading', 'innovative', or 'cutting-edge'—is generally not actionable because it is understood as subjective opinion. AI washing claims involving measurable accuracy, safety outcomes, professional equivalence, or legal compliance are held to a higher standard: the FTC requires competent and reliable evidence to exist before the claim is published. Specific AI performance or compliance claims are not treated as puffery.
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