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Tell people when an interaction meaningfully involves AI, make that information easy to notice, and explain what the system does when users need more than a label to make an informed choice. There is no established wording or repetition schedule that works for every product: the available evidence supports testing disclosure in context, not assuming that more labels always build trust—or that they inevitably cause fatigue.
What an AI disclosure should help a customer understand
A useful disclosure does more than attach an “AI” badge. It helps a person recognize who or what is responding and set reasonable expectations about the interaction. Depending on the product, that may mean explaining the system’s role, what it can and cannot do, whether a human reviews or can take over, and how information shared with it is handled.
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The Federal Trade Commission’s September 2025 inquiry into companion chatbots asked seven companies about disclosures covering features, capabilities, intended audience, potential negative impacts, and data collection and handling. This is a useful set of questions for product teams, but the inquiry was an information-gathering step—not a general labeling standard or a finding that the companies violated the law.
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Disclosure can affect different outcomes in different ways. A 2025 conference presentation, “When AI Disclosure Backfires,” describes a randomized field experiment on an Asian automotive e-commerce platform. It covered 152,634 unique users over nine months, from November 2024 through August 2025, in a high-stakes setting where the presentation reports an average vehicle price of about $62,000. The reported effects of attributing review summaries to AI varied between consideration and purchase, and also depended on whether the summaries were balanced or positive-only.
That result is a reason to distinguish decision stages and content framing when evaluating a disclosure—not proof that AI labels generally help or harm trust. The study concerns a particular platform, market, product category, and type of AI-generated content. It does not establish how often every brand should repeat a label, compare a broad range of wording, or identify a formula for preventing trust fatigue.
How to design a clear disclosure
Put it where it changes understanding
Make AI involvement visible at the point where a customer encounters or relies on the AI-supported interaction. A label that users must search for is a weak way to correct a mistaken impression. Be especially clear when the experience could otherwise appear to be a human conversation, or when a person may use the output to make a consequential decision.
Name the AI’s role, not just its existence
Use plain language that says what the system is doing: for example, whether it is generating a response, summarizing material, or helping route a request. Add relevant limits and explain whether a human reviews the output or is available to help. Do not imply a human is responding when that is not the case.
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Explain relevant data practices in readable language
When the interaction involves user information, tell people what they need to know about its collection, use, or sharing. The FTC’s January 2024 guidance warns that burying material information about consumer-data use in hyperlinks, legalese, or fine print can create legal risk. That warning concerns consumer-data practices; it is not, by itself, a universal rule for every AI label or jurisdiction.
Make repetition purposeful
Do not treat a one-time notice as sufficient in every journey, or repeat the same notice at every step by default. Consider whether the user’s understanding may change—for example, when moving from browsing to relying on advice, sharing sensitive information, or reaching a consequential decision. Test whether a reminder at that point improves recognition and understanding. The cited evidence does not set an optimal frequency.
How to evaluate whether a label is working
Assess more than a single trust rating. Test whether people notice the disclosure, understand the system’s role, and have accurate expectations about its capabilities, limits, human involvement, and data handling. Where relevant, examine behavior at distinct stages, such as initial consideration and final purchase; the automotive experiment shows that effects can differ by stage and by how the underlying content is framed.
Rank #4
Compare placement, specificity, timing, and repetition in the actual user journey. These are practical evaluation dimensions, not a ranking established by a head-to-head study. Avoid treating a change in one survey score as proof that the disclosure is clear, or as evidence of trust fatigue on its own.
A label does not make misleading content acceptable
Disclosure is not a cure for false claims or fabricated reviews. In its August 2024 announcement of the final rule on fake reviews and testimonials, the FTC addressed fake or false reviews, including AI-generated fake reviews. Calling a fabricated testimonial AI-generated does not make it truthful or remove the underlying concern about deception.
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What the evidence says about trust fatigue
The cited sources do not directly quantify consumer trust fatigue or establish a universal label, repetition threshold, or cross-jurisdiction legal standard. They support a more practical approach: disclose AI involvement honestly when it matters, make material context understandable, and test whether people notice and comprehend the information in the setting where they use the product.
The FTC materials described here concern US consumer-protection activity. Its 2025 chatbot inquiry is not itself a general AI-label mandate, and its review rule concerns reviews and testimonials. For a particular product or market, check current applicable law and authoritative guidance before making a compliance claim.
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