AI Usage Scale
EN

Specification · 1.2.0

How it is defined, and how it talks to everything else.

This is a declaration standard, not a proof standard. It cannot be verified by cryptography and it is not trying to be. Neither can a byline.

Decision procedure

Five questions, asked in order. The first one that terminates gives the level.

#Question YesNo
1 Was any generative AI used in making this work? next 0
2 Did AI produce new material that appears in the published work — beyond mechanically processing what you had already made? next 1
3 Did you make most of the final form yourself — most of the words, pixels, or sound? 2 next
4 Is the substance yours — the knowledge, the data, the argument, the direction? 3 next
5 Did a human review this before publication and take responsibility for what it says? 4 5

Surfaces

Optional. A single number describes the work as a whole. When parts of it differ sharply, declare them. The badge always shows one number; the breakdown lives behind the link.

Headline rule. The headline level describes the part of the work that carries its meaning. Decorative material that a reasonable audience would not consider material to the work does not drive the headline — declare it here instead.

Recognised surfaces: textimageaudiovideocodedata

{
  "aiUsageScale": "1.0",
  "level": 3,
  "surfaces": { "text": 3, "image": 5, "audio": 0 }
}

Edge cases

Every one of these came from trying to break the decision tree. They are settled here so that two honest people reach the same number.

Translation

A faithful translation inherits the level of the source work and adds a translation note. Translating a Level 1 article with a model publishes it as Level 1, translated by AI.

The substance and structure are unchanged. Penalising translation would make the world's writing less available, which is the opposite of the point.

Transcription

Transcribing speech you recorded is mechanical processing. It does not raise the level.

The words are yours. The model only wrote them down.

Substance from AI, wording from you

If a model originated the substance that materially determines what the work says — the thesis, the argument, the structure — it produced new material that survives into the work, even if you wrote out every word yourself. That is Level 2 or above, never Level 1. Feedback, critique, and fact-finding you verified stay Level 1: an editor's advice does not make the editor an author.

Ideas are material. Restating them in your own words does not un-invent them — and Level 1's promise that the model invented nothing has to mean it.

Decorative assets

A generated header image on a human-written article does not make the article Level 4. Declare the image under Surfaces.

The headline level describes what carries the meaning. Otherwise one stock image would swallow the whole declaration, and people would simply stop declaring.

Derivative works

Declare the level of your own contribution, and cite the level of the source if it declared one.

Live generation

Content generated on demand and shown to a user without review is Level 5, even if the system prompt was written carefully by a person.

Non-generative machine learning

Autofocus, denoise, upscaling, colour matching, and classification that invent nothing are not generative AI. They do not move you off Level 0.

If you doubt a declaration

A declaration is falsifiable in public. Check the link and the metadata; compare the declared level against the work and, where they differ, against the Surfaces breakdown; then ask the author — in a comment, a review, an issue. Most mismatches are honest readings of a boundary, and the edge cases above settle most of those.

If a declaration is knowingly false, the standard's answer is the one that scales: the claim was public and linkable, and now it is falsified in public. There is no detector and no registry here, on purpose — reputation is the enforcement, exactly as it is for a byline.

Interoperability

This scale complements provenance standards. It maps to the established IPTC vocabulary and experiments with the candidate syntax discussed by the W3C AI Content Disclosure Community Group; that syntax is not a W3C standard.

W3C AI Content Disclosure Community Group

The W3C AI Content Disclosure Community Group is discussing candidate syntax and a four-part model, but has not published a W3C standard. This scale currently emits experimental ai-disclosure metadata aligned with that model; consumers must not treat it as standardised W3C markup.

IPTC Digital Source Type NewsCodes

Every level maps to an IPTC term, so a declaration can travel into C2PA manifests, XMP, and the metadata Meta, Pinterest, and Google already read. The mapping is lossy in one direction only: IPTC cannot tell Levels 3, 4, and 5 apart.

C2PA / Content Credentials

Complementary, not competing. C2PA provides tamper-evident, signed provenance about an asset and its processing history. Its core specification does not attribute content to individuals or organisations, and provenance alone cannot determine whose thinking shaped a work. This scale is a declaration about contribution and review, designed to work for web text as well as media files.

EU AI Act (Regulation (EU) 2024/1689), Article 50

Article 50(4) switches the disclosure duty for public-interest text off the moment a human reviews it and holds editorial responsibility — the same question this scale asks to separate Level 4 from Level 5. The Code of Practice publishes three EU icons, and specifies the basic one as a first layer expecting an interactive second layer behind it. A declaration on this scale is that second layer: a mark anyone can read, linked to a public definition. The icons are free to use and require no attribution; using them is optional, while the Article 50 duties are not.

Measures for Labeling AI-Generated Synthetic Content, and GB 45438-2025

China requires two layers at once: an explicit label a reader can see, and an implicit label in the metadata carrying the nature of the content — confirmed, possible or suspected AI-generated — plus the service provider and a content ID. That two-layer shape is what a badge plus ai-usage metadata already produces. The duties fall on internet information service providers and content distribution platforms, not on individual authors.

California AI Transparency Act (SB 942, as amended by AB 853)

California pairs a manifest disclosure on the surface with a latent disclosure inside the asset: provider name, system name and version, a timestamp, and an identifier linking the content back to the system that made it. AB 853 moved the operative date to 2 August 2026 to line up with Article 50; hosting-platform duties follow on 1 January 2027. The duties fall on large generative-AI providers and platforms, not on individual publishers.

Level mapping

#Experimental ai-disclosureIPTC
0 none digitalCreation
1 ai-assisted algorithmicallyEnhanced
2 ai-assisted compositeWithTrainedAlgorithmicMedia
3 ai-generated trainedAlgorithmicMedia
4 ai-generated trainedAlgorithmicMedia
5 autonomous trainedAlgorithmicMedia

The mapping is lossy in exactly one direction, and the loss is the point: IPTC cannot tell Levels 3, 4 and 5 apart. It has no term for whose substance this is, and none for whether a person read it. Those are the two questions readers actually care about, and they are the two this scale exists to answer.

Machine-readable form

The canonical English definitions live in one file and drive the badges and machine-readable output. Localised explanatory copy is maintained separately and checked for structural parity.

GET /levels.json · CC0

<!-- AI Usage Scale proposal; custom metadata, not a registered standard -->
<meta name="ai-usage" content="3">
<meta name="ai-usage-standard" content="https://usagescale.org">

<!-- Experimental W3C Community Group alignment; not a W3C standard -->
<meta name="ai-disclosure" content="ai-generated">
<link rel="ai-disclosure" href="https://usagescale.org/3">

A site can also publish a whole-site declaration at a well-known path, where a crawler knows to look without being told:

GET /.well-known/ai-disclosure.json

{
  "aiUsageScale": "1.0",
  "level": 3,
  "surfaces": { "text": 3, "image": 0 },
  "definition": "https://usagescale.org/3"
}

Prior art

Nothing here is unprecedented, and pretending otherwise would be its own small dishonesty.

AI Assessment Scale (AIAS)

Perkins, Furze, Roe, MacVaugh

Five non-hierarchical levels, adopted by hundreds of institutions in 30+ languages. AIAS describes what a student is permitted to do. This scale describes what an author did. The debt is direct: the principle that no level ranks above another is theirs, and it is the most important rule here.

Creative Commons

The three-layer model — a mark anyone can read, a page that explains it, a specification that pins it down — is theirs.

Human Provenance in Film

Three tiers, free, CC BY 4.0. Proof that a graded standard can launch in a hostile industry.