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Make AI involvement clear where it affects a customer’s understanding, and explain what the system does, its limits, any human role, and relevant data practices. There is no established universal label or repetition threshold that prevents trust fatigue: brands should test whether people notice and understand disclosures in the specific interaction.
What should an AI disclosure tell customers?
A label should let people understand who or what is responding, without implying that a human is involved when that is not the case. A bare “AI” badge may identify the technology but leave important questions unanswered. Depending on the experience, explain the AI’s role, what it can and cannot do, whether a person reviews or can take over, and how information shared with the system is handled.
The Federal Trade Commission’s 2025 inquiry into companion chatbots asked seven companies about disclosures concerning features, capabilities, intended audience, potential negative impacts, and data collection and handling. The inquiry is a useful set of design questions, not a general labeling rule or a finding that the companies violated the law. FTC: inquiry into AI chatbots acting as companions.
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Where should the disclosure appear?
Put the information where it can shape a user’s understanding: near the chatbot or AI-generated content, at the point of interaction. Users should not have to hunt through a help page or infer from the interface that a response came from AI. Keep material details readable and in plain language rather than hiding them in fine print, legalese, or a hard-to-find hyperlink. The FTC has warned that burying disclosures can create risk in the context of consumer-data use; that warning is not a complete statement of the law for every AI product or jurisdiction. FTC: AI companies should uphold privacy and confidentiality commitments.
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How much should a brand repeat the label?
There is no evidence here establishing an optimal frequency or a universal threshold at which repetition becomes trust fatigue. Use a disclosure when it materially helps a customer understand a change in who is responding, what content is AI-generated, or what the system is doing. Then test whether people still notice and understand it. Do not assume that displaying a badge once, or repeating it on every screen, works equally well in every product.
Why can the same disclosure have different effects?
A 2025 conference presentation, “When AI Disclosure Backfires,” describes a randomized field experiment on an Asian automotive e-commerce platform. It involved 152,634 unique users and ran for nine months, from November 2024 to August 2025. The presentation reports an average vehicle price of about $62,000 for this high-stakes purchase context. AI attribution had different effects at the consideration and purchase stages, and results also varied depending on whether review summaries were balanced or positive-only. These findings do not show that AI labels always increase or decrease trust, and they should not be generalized automatically to other products or regions. FTC Third Marketing and Public Policy Conference.
How should you test a disclosure?
Test the actual interface and the decisions customers make in it, rather than relying on a single trust rating. Compare disclosure options in context and assess whether users notice the label, understand that AI is involved, form accurate expectations about the system, and know what human help or recourse is available. Check behavior at meaningful stages—such as browsing, relying on advice, or completing a consequential action—because the field experiment shows that the decision stage and content framing can matter.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Visibility: Can a user see the disclosure without searching?
- Specificity: Does it explain the AI’s role and relevant limits, not just name the technology?
- Timing: Does it appear when it helps explain the interaction or content?
- Human involvement: Can users tell whether a person reviews, takes over, or is available?
- Data practices: Are relevant collection, use, or sharing practices explained clearly?
- Comprehension and behavior: Do users understand the disclosure and act with realistic expectations?
These are practical evaluation dimensions, not a ranking established by a head-to-head study. A small wording or placement change may help in one context and fail in another, so assess the design with the people who will use it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What an AI label cannot fix
Disclosure is not a substitute for truthful content. An “AI-generated” label does not make a fabricated review or misleading testimonial authentic. The FTC’s 2024 announcement of its final rule on consumer reviews and testimonials specifically addressed fake or false reviews, including AI-generated fake reviews. FTC: final rule on fake reviews and testimonials.
The cited FTC materials describe US consumer-protection activity. They do not establish a universal AI-label requirement across jurisdictions; brands making compliance decisions should check current rules and authoritative guidance that apply to their product and location.
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