Estimating the threat of AI-agent responding across online survey platforms

Recent research and advances in LLMs have led to widespread concern that AI agents could pose as human online survey-takers. However, it remains unclear how prevalent AI agents are on survey research platforms and how to effectively detect AI agents. We validated a series of AI detection tests that effectively separated verified-human participants from both prompted commercial and purpose-built AI agents. Using these tests, we collected surveys on seven online platforms and find wide variation in rates of respondents failing AI tests, ranging from 6 to 41% across platforms (compared to a 2.4% false-positive rate for in-person human participants). We further demonstrate that undetected AI agents can impact the results of online surveys. Our findings suggest that while there is an urgent need for AI detection tests and consistent, systematic monitoring of data quality on online platforms, some platforms currently appear to provide data with a low rate of AI agents.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-10-15
DOI
https://doi.org/10.1073/pnas.2606460123
Primary Topic
Survey Methodology and Nonresponse
Type
article
Field-Weighted Citation Impact
0.00
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article

Estimating the threat of AI-agent responding across online survey platforms

Oleg Urminsky, Craig R. Fox, Jonathan Elliot Bogard, Kianté A. Fernandez et al.
Proceedings of the National Academy of Sciences
Survey Methodology and Nonresponse
article

Estimating the threat of AI-agent responding across online survey platforms

Oleg Urminsky, Craig R. Fox, Jonathan Elliot Bogard, Kianté A. Fernandez, Stephanie Y. Chen, Alexander Kentaro Moore, Robert Walatka, Andrea Löw, Grace Zhang
article en

Abstract

Recent research and advances in LLMs have led to widespread concern that AI agents could pose as human online survey-takers. However, it remains unclear how prevalent AI agents are on survey research platforms and how to effectively detect AI agents. We validated a series of AI detection tests that effectively separated verified-human participants from both prompted commercial and purpose-built AI agents. Using these tests, we collected surveys on seven online platforms and find wide variation in rates of respondents failing AI tests, ranging from 6 to 41% across platforms (compared to a 2.4% false-positive rate for in-person human participants). We further demonstrate that undetected AI agents can impact the results of online surveys. Our findings suggest that while there is an urgent need for AI detection tests and consistent, systematic monitoring of data quality on online platforms, some platforms currently appear to provide data with a low rate of AI agents.

Proceedings of the National Academy of SciencesVol. 123(42)
Booth University College (CA), University of Illinois Chicago (US), London Business School (GB), College of Business Administration (LV)
Openalex Percentile: Top 6%
Survey Methodology and Nonresponse
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