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Understanding trust toward human versus AI-generated health information through behavioral and physiological sensing
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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International Journal of Human Computer Studies, Volume 209
Abstract
As AI-generated health information proliferates online and becomes increasingly indistinguishable from human-sourced information, it becomes critical to understand how people trust and label such content, especially when the information is inaccurate. We conducted two complementary studies: (1) a mixed-methods survey (N=142) employing a 2 (source: Human vs. LLM) × 2 (label: Human vs. AI) × 3 (type: General, Symptom, Treatment) design, and (2) a within-subjects lab study (N=40) incorporating eye-tracking and physiological sensing (ECG, EDA, skin temperature). Participants were presented with health information varying by source-label combinations and asked to rate their trust, while their gaze behavior and physiological signals were recorded. We found that LLM-generated information was trusted more than human-generated content, whereas information labeled as human was trusted more than that labeled as AI. Trust remained consistent across information types. Eye-tracking and physiological responses varied significantly by source and label. Machine learning models trained on these behavioral and physiological features predicted binary self-reported trust levels with 73 % accuracy and information source with 65 % accuracy. Our findings demonstrate that adding transparency labels to online health information modulates trust. Behavioral and physiological features show potential to verify trust perceptions and indicate if additional transparency is needed.
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Sun, X, Ma, R, Wei, S, Cesar, P, Bosch, J A & El Ali, A 2026, 'Understanding trust toward human versus AI-generated health information through behavioral and physiological sensing', International Journal of Human Computer Studies, vol. 209, 103714. https://doi.org/10.1016/j.ijhcs.2025.103714