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What should an AI-content label say?
Use wording that describes the actual process rather than a vague label that could mean anything from full generation to minor assistance. When you know which part of a composite asset was generated or changed, identify it.
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| What happened | Clearer label | What to clarify |
|---|---|---|
| AI created the image, clip, audio, or text | “AI-generated” | Identify the asset or passage the label applies to. |
| AI changed an existing asset | “AI-edited” | Briefly describe the change, such as a generated background or replaced passage. |
| Only part of an asset was generated | “This image contains AI-generated content, but was not completely AI-generated.” | Point to the generated portion when that can be established. |
The partial-generation wording is an example in the Coalition for Content Provenance and Authenticity (C2PA) User Experience Guidance for Implementers, not a universal legal safe harbor. Avoid a blanket “Made with AI” when it leaves readers unsure whether AI created the whole work or only a component.
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Put a plain-language disclosure where the audience first encounters or consumes the content. For video, that may mean a visible label at the relevant scene or at first exposure; for audio, an audible disclosure may be appropriate. For a composite, make clear which image, passage, scene, or text was generated or altered when you can identify it.
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For EU deepfake disclosures, European Commission guidance says the notice should be clear and distinguishable, understandable, and perceivable without special technical tools or a separate action. An embedded machine-readable mark by itself does not meet that audience-facing disclosure duty.
How do visible labels and provenance marks differ?
A visible or audible label communicates directly with people. A machine-readable mark is intended to help systems identify or process information about generated or manipulated content. One should not be treated as a substitute for the other where both are needed.
C2PA Content Credentials can record origin and change history in signed manifests. Version 2.4 adds an AI Disclosure assertion with machine-readable information such as model type and an optional human-oversight level. These credentials can help people and systems evaluate provenance, but they do not certify that a content claim is true. NIST’s 2024 report likewise cautions that transparency can contribute to trustworthiness without guaranteeing it, and that how people access and interpret transparency information matters.
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What does EU law require, and when?
As of 4 October 2026, the European Commission says the transparency obligations in Article 50 of the EU AI Act apply from 2 August 2026. The obligations differ by role and use case; this EU example is not a worldwide rule.
Providers of generative AI systems
Providers must ensure relevant generated or manipulated synthetic content is marked in machine-readable form and detectable. This is distinct from the disclosure a deployer may owe to an audience.
Deployers publishing deepfakes or certain text
Deployers must disclose deepfakes and certain AI-generated or manipulated text published to inform the public on matters of public interest, subject to scope and exceptions. For deepfakes, the Commission FAQ says disclosure is due no later than a person’s first exposure and must be perceivable without dedicated technical tools or actions.
The text obligation has an exception where the text has undergone human review or editorial control and a natural or legal person holds editorial responsibility for the publication. The Commission also identifies standard editing and assistive functions as examples that may fall outside certain marking duties. The precise treatment depends on the facts and the official guidance.
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Creative, satirical, and fictional works
For evidently artistic, creative, satirical, fictional, or analogous works, the deepfake disclosure obligation is limited to disclosing that generated or manipulated content exists in a manner that does not hamper display or enjoyment.
The Commission’s Code of Practice is a voluntary tool to help demonstrate compliance; it is not the source of the underlying legal obligations. The Commission has also published optional icons. Its icon page says user-testing informed the design and that results improved across all measures when the basic icon was accompanied by text such as “modified.” That does not establish one universally sufficient label or placement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to build a responsible labeling workflow
- Record what AI did. Note whether it generated the whole asset, changed an existing asset, or contributed only a specific component. Do not claim a level of precision you cannot establish.
- Write the audience-facing disclosure. Name the affected asset or portion and use “AI-generated” or “AI-edited” as appropriate. Add a short explanation of a material change when it helps people understand what they are seeing or hearing.
- Place it at first exposure. Make the notice visible or audible in the relevant context, without requiring a person to open metadata or use a special tool.
- Add provenance information where supported. Use machine-readable marking or signed provenance credentials as a complement to the plain-language disclosure, not as a claim that the content is authentic or accurate.
- Check the published result. Verify how the destination platform displays the notice and whether provenance information survives editing and distribution. Cross-platform display and retention are not established as consistent, so check the actual channel.
How to choose between labeling methods
- Audience visibility: Can people notice and understand the disclosure at first exposure without extra technical steps?
- Specificity: Does the wording distinguish full generation from partial editing or enhancement?
- Machine readability: Can software detect and process the mark or provenance data where that is needed?
- Persistence and verifiability: Does the provenance information survive the workflow and allow validation of its signed history?
- Context and legal fit: Does the relevant duty apply to this asset, its publication purpose, audience, jurisdiction, and editorial-review status?
What a label cannot establish
A disclosure describes AI’s role; it does not settle whether a statement, image, or recording is accurate. A watermark, detector result, or provenance credential is not a truth certificate either. NIST warns that transparency may create false confidence, while C2PA credentials are designed to document provenance and edits rather than validate the truth of content claims. Keep factual review and labeling as separate responsibilities.
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