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Quick test: an announcement is credible when it connects a specific, currently available capability to a named part of the product, evidence that matches the promise, and clear limits. “AI-powered” alone proves very little. Ask what task the system performs, where a model is used instead of ordinary software rules or people, how the result was measured, and what happens when it fails.
AI-washing is a problem with the accuracy of a claim—not with the mere use of AI terminology. The method below helps you examine launches, product pages, investor statements and press releases without treating an unclear description as automatic proof of misconduct.
What is AI-washing?
AI-washing is misleading or overstated communication about a system’s artificial-intelligence functionality, performance, autonomy or originality. The central question is whether the company’s words match a real, identifiable capability in the product and the evidence supplied for it.
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A feature can legitimately use a machine-learning model and still be marketed deceptively. Conversely, a sparse technical description, use of a third-party model, human review, or conventional automation does not by itself establish wrongdoing. Those facts become important when they contradict what the announcement promised.
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The Australian Competition and Consumer Commission (ACCC) notes that AI claims can influence what consumers are willing to pay. Its discussion concerns Australian consumer risks; it is not a universal legal finding about every technology announcement. Terminology is also changing: the ACCC’s December 2025 snapshot observes that companies use agent and agentic for different levels of functionality. Treat the label as a question to investigate, not as proof that a claim is false.
Gary Gensler, then chair of the U.S. Securities and Exchange Commission (SEC), summarized the governing principle in March 2024: “they should say what they’re doing, and do what they’re saying.”
How can you recognize AI-washing quickly?
Use this five-question screen before accepting an AI claim:
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- What exact task does the AI perform? Look for an input, a processing step and an output you can describe in one sentence.
- Where is the model in the workflow? Separate model output from rules, search, data supplied by another vendor and human decisions.
- What evidence matches the promise? A claim about accuracy, speed, safety or productivity needs a relevant test set, metric, baseline, product version and use context.
- What are the limits? Check availability, permissions, failure handling, human override, data boundaries and third-party dependencies.
- Is the wording narrower than the headline? “First,” “best,” “fully autonomous” and “proprietary” each need a defined comparison and scope.
If an announcement cannot answer these questions, classify it as unsubstantiated or unclear rather than immediately calling it fraudulent. Preserve the distinction between an allegation, a regulator’s proposed order and a settled finding.
How do you dissect an announcement sentence by sentence?
Start by preserving the original wording and date. Record the product name and version, whether the feature is available now or merely planned, and the exact benefit promised. A roadmap statement and a deployed capability are different propositions.
A fictional example
Consider this deliberately fictional sentence: “Our proprietary autonomous agent is the world’s first AI operations platform, delivering 98% accurate results and eliminating human work.” Break it into separate claims:
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| Words in the claim | Questions to ask | Evidence that would be relevant |
|---|---|---|
| “Proprietary” | Which component is owned or developed by the company? Is the model, training data, orchestration code or user interface actually proprietary? | Architecture description, licensing information and a clear explanation of third-party services. |
| “Autonomous agent” | Which independent actions can it take, with what permissions, and where must a person approve or correct it? | A workflow diagram, permission boundaries, intervention points and logs from representative tasks. |
| “World’s first” | First among which products, geography, industry, date and definition of “AI operations platform”? | A stated comparison set and a defensible method for establishing priority. |
| “98% accurate” | Accurate on what task, labels and sample? What was the baseline and how were ambiguous cases handled? | Test-set size and composition, metric definition, model or version, date, confidence intervals where appropriate, and results against a simple or human baseline. |
| “Eliminating human work” | Does the system replace every step or only draft, triage or recommend? Who handles exceptions and safety-critical decisions? | A task-by-task responsibility map, escalation policy and evidence from the intended operating environment. |
The sentence may contain a real model while still overstating ownership, autonomy, priority or business impact. Evaluate each proposition independently instead of accepting or rejecting the entire announcement as one package.
What evidence makes an AI performance number meaningful?
A percentage without context is not a useful performance claim. Ask for at least five items:
- Task and population: What was classified, generated, predicted or detected, and on whose data?
- Metric: Does “accuracy” mean exact match, a thresholded classification, precision, recall, error rate or a business measure?
- Test set: How large was it, who selected it, and does it resemble real inputs rather than curated demonstrations?
- Baseline: How did the system compare with a simpler rule, an existing product or trained human reviewers?
- Conditions and version: Which model, prompt, tools, date, language, hardware and operating constraints produced the result?
Also ask about important subgroups and failure cases. A high aggregate score can hide poor performance for a language, disability-related input, unusual document type or high-risk edge case. NIST’s AI Risk Management Framework Playbook recommends measures that fit the purpose, documented limits on generalizability, and comparisons with simpler or human baselines. Its Map, Measure and Manage guidance also emphasizes defining the task and method, documenting human roles and monitoring performance and third-party risks over time.
For example, a vendor’s “98% detection accuracy” could mean 98 of 100 carefully selected academic samples were classified correctly. That does not tell you how the system performs on general web content, short messages, code, other languages or adversarial inputs. The missing denominator and context are the issue—not the presence of a number itself.
How should you read words such as “first,” “best” and “fully autonomous”?
Read superlatives literally and supply the comparison the announcement leaves out. “Best” needs a metric and a field of competitors. “First” needs a category, a date and a way to verify earlier products. “Fully autonomous” needs a list of actions the system can complete without approval, including the permissions and recovery process. “Proprietary” needs a component-level explanation.
On March 18, 2024, the SEC announced settled charges against Delphia (USA) Inc. and Global Predictions Inc. The SEC described misleading statements about purported AI use, including claims about using client data in an investment process and Global Predictions’ description of itself as the “first regulated AI financial advisor.” The firms agreed to settle without admitting or denying the SEC’s findings and paid $400,000 in combined civil penalties. This is a U.S. investment-adviser example; it is not a finding about ordinary consumer devices or every company that uses a superlative. Read the SEC announcement for the agency’s precise allegations and resolution.
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What did regulators say about AI-washing cases?
SEC: statements about purported AI use
The SEC case shows why the underlying representation matters. Saying that client data is used in an AI investment process is a factual claim about how the service operates. Calling a firm the first regulated AI financial adviser is a separate priority claim. Both need to be assessed against actual practice and a defined comparison set. The settlement’s jurisdiction and facts should not be generalized into a finding that all AI marketing is deceptive.
FTC: a performance claim that changed with the test context
In an April 28, 2025 release, the U.S. Federal Trade Commission (FTC) described advertising for Workado that claimed 98% AI-text-detection accuracy. The FTC release also described independent testing that found 53% accuracy on general-purpose content. According to the agency, the underlying model had been trained or fine-tuned to classify academic content effectively. The difference illustrates why a test set and intended use must accompany a percentage.
The FTC described the matter as a proposed order at publication. Do not present that release as proof of a then-final order or assume its current procedural status without checking the agency’s docket. Its account is available in the FTC release.
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How do you map AI to a real product workflow?
NIST’s Map guidance suggests defining the task and method and documenting human roles, limitations and components. Apply that approach in a simple input-to-output map:
- Input: Identify the data, files, sensor readings, prompts or events entering the system.
- Model step: Name the model-based operation—such as classification, retrieval, prediction, generation or ranking—and identify whether a third-party service performs it.
- Non-AI steps: Mark deterministic rules, database lookups, templates, search indexes, integrations and manual review separately.
- Output: State exactly what the user receives and whether it is a recommendation, draft, decision, alert or automated action.
- Uncertainty and control: Find confidence handling, refusal behavior, escalation, rollback and human approval requirements.
A third-party model or human review is not inherently deceptive. It becomes a potential misrepresentation if the company says it built a proprietary model, promises end-to-end autonomy, or implies that a human never intervenes when those statements are untrue. Likewise, a system can contain a genuine AI component without outperforming a simpler algorithm.
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What should you check about availability and ongoing operation?
Separate what exists today from what is announced for a future release. Confirm the product version, supported countries, account tier, waitlist status, required integrations and any usage limits. A demonstration, preview or pilot does not establish general availability.
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- Documented use boundaries and prohibited tasks.
- Human override and escalation paths.
- Monitoring for drift, degraded accuracy and unusual inputs after deployment.
- Disclosure of model, data, cloud and integration dependencies.
- Change logs showing when the model or evaluation method changed.
- Recovery procedures when an automated action is wrong.
NIST’s Measure and Manage material recommends tracking whether a system meets its objectives and managing third-party risks over time. A missing technical paper may justify a request for clarification, but secrecy alone does not prove misconduct; security, intellectual-property and competitive concerns can limit disclosure.
What is not automatically AI-washing?
Do not label a claim AI-washing solely because:
- The product combines machine learning with rules or conventional automation.
- A hosted model from another provider performs one component.
- People review, edit or approve outputs.
- The company does not publish every model weight, prompt or training datum.
- The marketing uses a new term such as “agentic” without a universally fixed definition.
These facts should prompt precise questions about the promised capability. The concern arises when the announcement materially misstates them or uses an impressive label to imply a capability, result or ownership that the evidence does not support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is a practical checklist for journalists, buyers and investors?
Save the announcement as it appeared, including date, URL, screenshots and product version. Then work through this checklist:
- Quote the exact promise and identify whether it describes a present feature, preview or roadmap item.
- Rewrite the promise as a testable statement with a subject, task, metric and time period.
- Draw the workflow from input to output, marking models, rules, people and third-party services.
- Request evaluation details: sample, labels, metric, baseline, conditions, version and date.
- Ask how results vary across relevant users, languages, document types and failure cases.
- Define every superlative and comparison set.
- Check permissions, intervention, fallback and monitoring after deployment.
- Search regulator statements and distinguish allegations, proposed orders and settled findings.
- Write the conclusion narrowly: supported, unsupported, contradicted by available evidence, or unknown.
If the product is being considered for a high-impact decision, require documentation and independent or reproducible evaluation proportionate to the risk. Do not treat a polished demo, a celebrity endorsement or a large percentage as a substitute for task-specific evidence.
Where can you read more about separating real AI from hype?
NIST’s AI RMF Playbook provides a useful Map–Measure–Manage framework for defining tasks, evaluating performance and monitoring deployed systems. The pages currently note that AI RMF 1.0 is being updated, so treat the cited Playbook as the version consulted rather than as a newly finalized revision.
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For a broader, non-regulatory treatment of capability limits, O’Reilly lists AI Snake Oil as a book by Arvind Narayanan and a coauthor. It is optional background reading, not evidence about any particular announcement.
How should you state your conclusion?
Use language that matches the evidence. “The company has not disclosed a test set” is different from “the claim is false.” “The regulator alleged misleading statements” is different from “the company was found guilty.” “The feature is described as forthcoming” is different from “the product currently does this.”
AI-washing analysis is strongest when it links each disputed word to a verifiable product behavior, a relevant measurement and a stated limit. That approach protects readers from inflated promises without turning uncertainty, ordinary software engineering or legitimate use of outside models into accusations.
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Frequently Asked Questions
Is using a third-party AI model automatically AI-washing?
No. Many products legitimately combine a hosted model with their own data, rules and interface. It becomes misleading when the announcement claims an entirely proprietary system, conceals a material dependency or attributes the provider’s capability to work the company did not perform.
How can an investor verify an AI claim that has no public technical paper?
Ask for a dated product version, a plain-language workflow, evaluation results against a relevant baseline and an explanation of human approvals and failure handling. If the company cannot disclose sensitive details, record the limitation and avoid treating the claim as independently verified.
Does an “agentic” product have to work without any human involvement?
No universal definition requires that. Ask which actions it can initiate, what permissions it receives, where approvals are required, and how it recovers from an incorrect action; the label alone does not answer those questions.
What should a buyer request before paying a premium for an AI feature?
Request evidence that the feature improves the specific task you care about compared with your current process or a simpler alternative. Also check data-retention terms, model-change notices, service dependencies, audit logs and a way to disable or roll back automated actions.
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