AI disclosure

“AI-assisted” is the disclosure nearly every policy I know would accept, and it is close to useless. It satisfies the letter of the rule while saying nothing about what I reasoned through and what I handed off, which is exactly the gap my research is about. So here’s the longer version.

This site was rebuilt with Claude, in Claude Code, and Codex, over a few working sessions in August 2026, when I was procrastinating over some other work... The model wrote most of the code you are looking at: the three-column layout, the hover reveals in the research list, the figure lightbox, the photo easter egg behind my headshot, the typography setup, and the scripts used to verify all of it. It deleted the previous design’s components once this one replaced them. It also ran an accessibility audit that caught four text-contrast failures sitting just under the WCAG AA threshold (that I just learnt about), in code it had written for me earlier the same day.

The research, and every claim this site makes about it. Each project summary began as my own notes: the conversation with Jessica that became the disclosure work, the hours of thematic analysis behind the qualitative tooling, the coding agent that informed me a file I was actively editing was “apparently not in use.” The model tightened that prose; I sent it back whenever it stopped sounding like me.

I also decided what the site should feel like, which parts to cut, when something was finished, and which of the model’s suggestions were wrong. Those calls don’t hand off well.

This is the part disclosures usually leave out. Partway through, the model decided the hand-drawn underline under my headline was broken. It wasn’t. It was looking at screenshots from a headless browser that renders that element badly, and based on those it rewrote working code three times, sounding more sure of itself each time.

I opened the page on my own screen, saw the underline right where it belonged, and told it to put everything back. Nothing in the model’s output hinted that it was wrong. I only caught it because I looked.

This statement took longer to write than several of the features it describes, and that’s kind of the point. Honest attribution means reconstructing decisions you made quickly and half-consciously, and writing down the moments where you didn’t come out looking sharp. Ticking an “AI-assisted” box takes no effort. This took real effort, and that effort falls on whoever is being the most honest.

Whether that burden is fair (between students and instructors, or between the people who write disclosure policies and the people who have to follow them) is what my collaborators and I are working on, most recently in a CRAFT session at FAccT 2026.

If you’re drafting a policy, or you’re a student trying to work out what’s fair to write down, I’d ask less about whether you used AI and more about what you decided, and how you’d know if you were wrong.