Evidence map›Paper›PMID 42879471›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

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

Grace Zhang, Robert Walatka, Stephanie Y Chen, Alexander K Moore, Oleg Urminsky, Kianté Fernandez, Andrea Low, Jonathan E Bogard, Craig R Fox

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Estimating the threat of AI-agent responding across online survey platforms.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Grace ZhangMarketing, University of Chicago Booth School of Business, Chicago, IL 60637.ORCID 0000-0002-8315-9687
Robert WalatkaBehavioral Science, University of Chicago Booth School of Business, Chicago, IL 60637.ORCID 0009-0003-9887-7053
Stephanie Y ChenMarketing, London Business School, London NW1 4SA, United Kingdom.ORCID 0000-0002-6548-366X
Alexander K MooreMarketing, University of Illinois Chicago College of Business Administration, Chicago, IL 60607.
Oleg UrminskyMarketing, University of Chicago Booth School of Business, Chicago, IL 60637.ORCID 0000-0003-4390-386X
Kianté FernandezDepartment of Psychology, University of California, Los Angeles, CA 90095.ORCID 0000-0002-8493-880X
Andrea LowBehavioral Decision Making Area, Behavioral Decision Making Area, Anderson School of Management, University of California, Los Angeles, CA 90095.ORCID 0009-0007-0684-6305
Jonathan E BogardOrganizational Behavior, Olin Business School, Organizational Behavior, Olin Business School, Washington University in St. Louis, St. Louis, MO 63130.ORCID 0000-0002-2700-7427
Craig R FoxDepartment of Psychology, University of California, Los Angeles, CA 90095.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceHumansInternetLarge Language ModelsSurveys and Questionnairesagentic AIbot detectionlarge language modelssurvey qualitysurveys

Identifiers

PMID42879471
PMCPMC13684237

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.