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Disclose AI at the moment a reasonable person could mistake the system, media or message for human-made. Tell users before or at the first chatbot exchange; put a visible or audible label on synthetic or manipulated media; identify AI-generated public-interest text when the required human-review exception does not apply; and place advertising disclosures next to the claim they qualify. Keep machine-readable provenance as a second layer, not a substitute for what people can see or hear.
This guide turns the European Commission’s Article 50 transparency rules, U.S. Federal Trade Commission (FTC) disclosure guidance and the Interactive Advertising Bureau’s 2026 risk framework into an implementation plan for product, editorial and marketing teams.
What does clear AI disclosure require?
A useful disclosure answers four questions immediately: What is artificial? Where did AI participate? When will the person encounter it? and Who remains responsible? The notice should be understandable without opening a policy page, downloading a file or using special software.
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- Use the right timing. Article 50 requires information at the latest at first interaction or exposure. A footer added after a conversation starts is too late for a direct-interaction notice.
- Use specific language. “AI used” is weaker than “You’re chatting with an AI assistant” or “This voice was generated with AI.” Identify whether AI generated, transformed, recommended or merely assisted.
- Make it perceivable. Notices must remain readable, audible where relevant, high enough in contrast and available to screen readers and other assistive technology.
- Keep the claim and qualification together. FTC guidance treats a disclosure as ineffective when it is hidden in fine print, a dense agreement, a comment, a vague hyperlink or a location that contradicts the overall impression.
- Assign ownership. Record who approved the wording, who reviewed the content and who is accountable for changes when a model, platform rule or law changes.
When must a product tell someone that a chatbot is AI?
Under Article 50, providers must inform users when they are interacting directly with an AI system unless that fact is genuinely obvious from the circumstances. The European Commission describes four cumulative conditions: the system qualifies as AI, is designed for genuine two-way exchange, communicates directly with the person and interacts with natural persons.
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Show the notice in the conversation surface before the first answer, or at the exact start of the first exchange. The Commission says people should be notified “from the start of the first interaction in a clear and distinguishable manner.” Do not rely on a brand icon, robotic voice, futuristic styling or a generic terms-of-service link to make AI involvement supposedly obvious.
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Place a short sentence above the input box and expose the same text to assistive technology:
You’re chatting with an AI assistant. A human may review conversations to improve support.
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Only include the second sentence if it is accurate for your service. If a person takes over, announce the change in the transcript, for example, You’re now chatting with Maya, a human support specialist. If the conversation returns to an automated agent, identify that transition too. Keep the notice in the interaction surface rather than only in a privacy policy.
When the “obvious in context” exception is risky
The exception is narrow in practice. An explicitly labeled fictional character in a clearly marked game may be more obvious than a support representative, financial guide or health information agent. If a reasonable user could believe a real employee is replying, disclose anyway; the cost of a short notice is lower than correcting a misleading impression later.
How should generated images, video, audio and voices be labeled?
Use a human-facing label wherever viewers or listeners encounter synthetic or manipulated media, and preserve machine-readable marking supplied by the generation system. Article 50 distinguishes these layers: providers must make generated outputs detectable as artificially generated or manipulated, while a deployer’s disclosure of a covered deepfake must still be perceivable without special tools or extra actions.
| Content | Human-facing example | Implementation detail |
|---|---|---|
| Image | AI-generated image |
Place the label in the image frame, caption or adjacent player area where it remains with the image when shared. |
| Video | This video contains AI-generated scenes |
Show the text on screen long enough to read; repeat it in the description when the video can be viewed without the opening frames. |
| Voice or audio | This voice was generated with AI |
Provide an audible statement for audio-only distribution and a text equivalent for players and transcripts. |
| Manipulated or deepfake media | AI-manipulated video |
Put the label where the audience encounters the media. Hidden metadata alone does not meet the deployer’s user-facing duty. |
Keep the provider’s machine-readable mark or provenance metadata intact when exporting, editing and reposting the file. Test whether common resizing, transcoding or content-management workflows strip it, and document what happens. That technical mark supports detection by platforms and tools; it does not replace the visible or audible disclosure a person needs.
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Disclose AI-generated or manipulated text published to inform the public about a matter of public interest when it has not undergone human review or editorial control and a person or organization holds editorial responsibility. This rule targets the risk that readers will treat an unreviewed synthetic account as an accountable human or newsroom statement.
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Determine whether the exception applies
- Identify whether the text concerns a matter of public interest rather than a private internal memo or routine product copy.
- Record whether a qualified person reviewed the substance, not just spelling or formatting.
- Record whether an editor or organization exercised editorial control and accepts responsibility for publication.
- If those conditions do not clearly satisfy the applicable exception, disclose the AI role.
Describe the role accurately. If a model drafted the substantive claims and a person checked them, say so; do not describe the process as “AI-edited” if AI actually generated the report. A practical note might read: AI-generated draft reviewed and edited by our newsroom; the publisher is responsible for the final text.
How should advertisements and product claims disclose AI use?
FTC guidance applies the same consumer-protection principles online as in other media. When a disclosure is needed to prevent deception, make it clear and conspicuous so consumers notice and understand it. Put it close to the claim, use readable text or intelligible audio, and ensure the overall impression does not contradict the qualification.
Placement by channel
- Static ad: Put a short disclosure beside the AI-generated testimonial, image or product demonstration, not in a distant footer.
- Short-form video: Show the notice on screen and keep it visible long enough to read. If the claim is communicated by voice, say the disclosure aloud as well.
- Audio ad or podcast: Speak the disclosure at a pace and volume comparable to the claim; do not hide it in a barely audible tag.
- Sponsored social post: Place the AI qualification in the post or creative itself. A comment, profile bio or expandable “more” section is easy to miss.
- Product page: Qualify the specific AI-generated demonstration, review or image next to that element. A general policy page cannot correct a misleading product impression by itself.
The FTC’s mobile-app guidance says users are more likely to continue doing business with a company that gives them “the straight story up front.” Treat that as a design requirement: short, prominent and close to the decision the consumer is making.
What is the difference between a human-facing label and a machine-readable mark?
They solve different problems and should be managed separately.
| Dimension | Human-facing disclosure | Machine-readable marking |
|---|---|---|
| Audience | People viewing, hearing or interacting with the content | Platforms, search tools, moderation systems and forensic software |
| Form | Visible text, icon with explanatory text, spoken notice or transcript | Embedded metadata, provenance record or another detectable technical signal |
| Timing | At first interaction or exposure, or beside the relevant claim | Applied when the system generates or transforms the file and preserved through handling |
| Accessibility | Must work without special tools and support disabilities | Not a substitute for a perceivable explanation |
| Deepfake disclosure | Required for the deployer’s audience-facing notice | Supports detection but does not, by itself, satisfy that notice |
A “machine-readable only” strategy fails the human communication test. Conversely, a caption without preserved provenance can make automated detection and downstream moderation harder.
Who is responsible: provider, deployer, publisher or advertiser?
Responsibility follows the role your organization performs, and one organization can hold several roles at once.
| Role | Typical duty | Operational owner |
|---|---|---|
| AI provider | Inform direct users of AI interaction and support marking or detection of generated outputs | Product and engineering |
| Deployer | Give people a perceivable disclosure when publishing or presenting covered synthetic or manipulated content | Content, product or channel owner |
| Publisher or editor | Disclose unreviewed AI-generated public-interest text when the applicable exception is not met | Editor-in-chief or designated reviewer |
| Advertiser or marketer | Make material AI qualifications clear and conspicuous near the claim | Marketing and legal/compliance |
Do not assume a vendor’s label transfers your entire obligation. Check what the provider marks, what your distribution channel removes and what your audience can actually perceive.
How do you build an AI-disclosure workflow?
Use a documented workflow rather than asking authors or developers to improvise a label at publication time.
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- Inventory touchpoints. List chat interfaces, agents, avatars, generated files, editing tools, recommendation features, public-interest publishing and every advertising placement.
- Classify the touchpoint. Mark it as direct interaction, generated content, manipulated content, biometric or emotion-recognition exposure, advertising disclosure or internal-only assistance.
- Assign the legal and operational role. Note whether your organization is provider, deployer, advertiser, publisher or several of these.
- Assess authenticity risk. Ask whether a reasonable audience could mistake the output, speaker, image or claim for human or authentic.
- Select wording and placement. Specify the exact text, language, contrast, size, timing, audio treatment and screen-reader behavior before production.
- Preserve technical provenance. Keep machine-readable marking supplied by the generation system and test exports, edits and uploads for stripping.
- Document review and responsibility. Record who checked factual substance, who holds editorial responsibility, which exception was relied on and who approved release.
- Test with representative users. Check comprehension, mobile layouts, video timing, audio intelligibility, keyboard navigation and screen readers. Retain screenshots, scripts and version dates.
- Recheck on change. Review the policy when a law, regulator interpretation, platform rule, model or generation tool changes.
Where should an AI notice appear in common product experiences?
Place the notice at the point of first exposure, then repeat it when context changes.
Chat and voice agents
Put the statement above or inside the conversation before the first response. In voice-only flows, play the notice before the user is invited to rely on the answer, and provide a persistent text equivalent in the app or transcript.
Avatars and digital presenters
Label the presenter in the opening frame and adjacent description. A human-like face or voice increases the chance of mistaken identity, so visual style is not a replacement for disclosure.
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Generated media libraries
Attach the label to thumbnails, detail views and downloaded descriptions. If users can crop or repost media, include the disclosure in the original caption or frame as well as metadata.
Mixed human-and-AI workflows
Explain the division of labor: for example, “AI-generated draft reviewed by our editor.” Avoid implying that a human personally created every sentence, image or voice when that is not true.
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Clarity is a combination of words, design and timing, not a legal phrase hidden in a policy.
- Use everyday terms such as “AI-generated,” “AI-manipulated” or “You’re chatting with an AI assistant.”
- Place the notice beside the relevant interaction or claim, with enough contrast and size for the device.
- Do not bury it in fine print, a long license agreement, dense legal copy, a comment or a vague hyperlink.
- For video, keep text on screen long enough to read; for audio, speak at a normal pace and volume.
- Expose equivalent text to screen readers and captions to people who cannot hear audio.
- Translate the notice for the audience’s language and check that translation preserves the AI role, not merely the word “synthetic.”
- Do not use an icon alone unless its meaning is immediately explained in text.
Run a simple comprehension test: show the experience without explaining it, then ask users what they believe created the content and when they noticed the notice. If answers vary or users miss it, change the design rather than adding more legal text.
What dates and jurisdictions should teams plan for?
European Union Article 50 transparency obligations apply from 2 August 2026. The European Commission describes a limited grace period until 2 December 2026 for marking and detection obligations for certain AI systems placed on the market before 2 August 2026. That later date does not remove the need for direct-interaction information or deployer-facing disclosures.
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Those dates describe EU requirements; they do not automatically determine obligations elsewhere. In the United States, FTC deception principles apply to advertising and commercial communications without waiting for a single AI-specific effective date. The FTC’s online and mobile guidance still requires a straight, prominent explanation when silence would mislead consumers.
The Interactive Advertising Bureau published AI Transparency & Disclosure Framework V2 on 18 August 2026. It offers a risk- and materiality-based industry decision aid covering AI-generated and AI-assisted text, imagery, video, audio, synthetic voices, digital twins and AI-powered consumer interactions. Treat the framework as practical guidance; applicable law and regulator instructions control.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common disclosure failures?
Failure: one generic footer for everything
Fix: Match the notice to the event. A chatbot needs a first-interaction statement; a deepfake needs a visible or audible label; an ad needs a qualification next to its claim.
Failure: metadata-only disclosure
Fix: Preserve metadata, but add a perceivable label wherever people encounter covered media.
Failure: “AI-assisted” when AI generated the substance
Fix: Describe the actual role and state who reviewed and approved the final material.
Failure: notice appears after the decision
Fix: Move it before the first exchange, first exposure or purchase-relevant claim.
Failure: inaccessible visual or audio notice
Fix: Provide synchronized text, captions, transcripts and screen-reader labels, then test them with assistive technology.
Failure: relying on a provider’s compliance
Fix: Verify what your own interface and distribution channel show. A provider’s technical mark does not automatically satisfy a deployer’s human-facing duty.
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What should an internal disclosure record contain?
For each AI touchpoint, retain a short record containing the system name and version, AI role, content type, audience, authenticity risk, required human-facing wording, machine-readable marking status, human-review status, editorial or business owner, approval date and next review date. Add screenshots, audio scripts, captions and export tests for high-risk material.
This record lets a team explain why a notice appeared where it did, demonstrate that review was meaningful and update hundreds of placements when a model or rule changes. It also prevents a one-time exception from silently becoming the default treatment for every future publication.
How can a team choose the right wording?
Start with the shortest truthful statement that removes the likely misunderstanding, then add one operational detail when it affects reliance.
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Do not promise human involvement that did not occur, imply that a watermark proves authenticity, or use wording so broad that users cannot tell what part of the experience is synthetic.
How should disclosure policy be maintained over time?
Assign a named owner for each product and channel, review notices whenever the underlying model or media pipeline changes, and schedule a formal legal and accessibility check at least when a new rule or platform requirement takes effect. Keep an inventory of approved phrases so designers and editors do not create conflicting labels.
Monitor real user reports: confusion about whether a person is replying, inability to hear a notice, or media that loses its label after download are signals to revise the workflow. The goal is not a single badge; it is a durable chain from generation to publication in which people receive an honest explanation and technical systems retain a detectable record.
Frequently Asked Questions
Do I have to disclose AI used only for spelling or grammar suggestions?
Not every internal assistance feature creates the same public-facing risk. If AI only corrected mechanics and did not generate or alter a material claim, a public label may not be necessary; document that limited role and apply a notice when the audience could reasonably infer human authorship of substantive content.
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Human approval does not erase the fact that the image is synthetic. Keep the visible or audible AI label and preserve machine-readable marking; human review can matter to a public-interest text exception, but it is not a substitute for identifying generated media.
Can a privacy policy count as notice that a user is talking to AI?
Usually not for first interaction. A policy may explain data handling, but the AI identity notice belongs in the conversation interface before or at the first exchange so users can understand the interaction without searching elsewhere.
What if a platform removes my AI label when it recompresses a video?
Treat that as a distribution defect. Keep the disclosure in the visible video or caption, test the platform’s export behavior, retain the test result and contact the platform or change the workflow if machine-readable provenance is being stripped.
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