AI-mediated contamination in online research: Taxonomy, risk gradient, and recommendations

For two decades, online research has relied on a quality heuristic: careful, coherent responding is good data. That heuristic is no longer reliable. Autonomous AI agents can now pass nearly all conventional quality checks, and on text-rich crowd-work tasks, reported use of large language models (LLMs) approaches one third. When such consultation shapes the response process itself—not merely its surface expression—the resulting data appear human-generated while embedding systematic, model-shaped distortions. I synthesize emerging evidence on how AI-mediated contamination varies across research settings in prevalence, mechanism, and inferential consequence, distinguishing three contamination pathways (full delegation, partial mediation, and spillover) and three vulnerability zones (text-rich tasks at highest risk, browser-based cognitive paradigms as an emerging vulnerability, and supervised or identity-vetted settings at lower risk). Even modest contamination can shift estimated public opinion, compress attitudinal extremes, and, over time, feed back into the training data that future models inherit. Current-reported platform countermeasures may raise the cost of contamination but have not been independently validated under adversarial conditions. I therefore argue for a shift from ad hoc detection to infrastructure redesign: contamination-aware sensitivity analyses, explicit stratification of data collection by evidential role, transparency norms that balance open science with adversarial robustness, and a minimum reporting checklist for online studies in vulnerable settings. I close by asking when AI mediation should be treated not as contamination but as part of the ecological baseline of human responding—a question that requires the field to specify the target cognitive system in any given study.