Disclose the review, not the tool

Nielsen Norman Group published a framework last week for deciding when to tell people AI was involved in your work. Taylor Dykes calls it PACED: policy, audience, context, expectations and degree of use.

It’s careful work and it names the awkward part honestly. People say they want disclosure and then think less of the person who gives it.

I’d use that framework tomorrow. I also think the question underneath it’s set up wrong, and a good framework built on a bad question inherits the problem.

“AI was used here” is a fact about a tool. Nobody on the receiving end is asking about the tool.

The label costs something and says nothing

Envato surveyed 1,780 creative professionals for Beyond Adoption. Fifty-eight percent have used AI on client work without telling the client. Only 31% always disclose.

The more interesting number is the reason. Of the people who said nothing, 48% said they don’t see why they should have to disclose every tool. That gets read as a rationalization. It’s half right.

Nobody itemizes Figma, the stock license, the plugin or the Slack thread where the idea actually came from. A tool name has never told a client whether the work is any good.

And the penalty for saying this particular tool name is measurable. Dykes cites the studies: ratings drop as the disclosed share of AI involvement goes up. The research Florida International University wrote up from Joel Carnevale, Anand Benegal and Lynne Vincent is the part that should stop people. Listeners marked a composition down when they were told AI was involved, and the hit landed whether they believed the piece came from Hans Zimmer or from a first-year music student.

The same penalty fell on a master and a beginner.

A signal that can’t tell those two apart isn’t carrying information about the work. It’s carrying a reaction to a category, and we’re building policy on top of it.

Provenance is not review

Article 50 of the EU AI Act became applicable on August 2, 2026. Synthetic audio, image, video and text have to be marked in a machine-readable format and detectable as artificially generated. Deepfakes get disclosed. A chatbot has to say what it is.

That’s provenance and it’s worth having. It answers whether a machine made the pixels, which is a real question for a voice in an ad or a face in a video.

It doesn’t answer the question a client, a reviewer or a design lead is actually asking. That question is whether anyone competent looked at this, and what they were looking for.

The review is happening off the books

In AI in Design 2026, the survey Designer Fund and Foundation Capital ran with 900 designers across 60 countries, 80% say they rely on their own quality judgment over the model’s. Half have pushed AI-generated code to production. Forty percent claim full ownership of AI-assisted work and another 43% say it’s mostly theirs.

So the review exists. It happens in one person’s head, it takes real skill, and it leaves nothing behind that anybody else can inspect.

Patrick Neeman got close to this in Craft Still Matters, arguing that you describe your standards and keep humans reviewing. What I’d add is that the describing has to land somewhere a reader can find it.

What I write instead

Nothing I hand over carries the words “AI-assisted”. The line I write instead says what I checked and what came back wrong.

Three sentences, at the top, before the work: here’s what I checked this against, here’s where it failed, here’s what I changed. If I can’t write those three sentences, naming the tool doesn’t rescue the handoff, and naming the tool is the only thing most disclosure policies ask me for.

I ran a whole-library audit at Best Egg and wrote the crawler’s rules myself. The rules are the reason anyone could argue with the output. A flag was checkable because the rule behind it was on paper, which meant the interesting conversation was about the rule and not about the crawler.

In an interview I ask for one piece of work and the thing the model got wrong on it. Not whether they use the tools. What the first answer was, why they threw it out and what they held it up against. A candidate who can’t answer that has been shipping unreviewed work, and no disclosure label would have told me.

Who stays quiet, and why

Dykes is right that being caught out hurts more than telling people yourself, and that’s a good reason to disclose when policy is pointed at you. It’s also why disclosure gets heard as a confession. A confession is a bad container for a claim about quality.

The Envato rates break along exposure. Agency and studio owners disclose least at 28%, behind freelancers at 31% and in-house designers at 35%. Europe is highest at 39% and the Middle East and Africa lowest at 22%.

The people with the most staff and the most to lose say the least. That’s what a label looks like when it carries risk and no upside.

You don’t fix that with a better decision tree about when to say the word. You fix it by changing what gets said, from a fact about the tool to a record of the judgment. A record gets more useful as the tools spread. The tool name is heading for describing everything, and a label that describes everything sorts nothing.

Fifty-eight percent are already not saying it. Arguing them into compliance is a smaller win than it looks. What a client can act on is the check, and not one of the frameworks in this argument asks anyone to write the check down.