Understanding Trust Toward Human versus AI-generated Health Information through Behavioral and Physiological Sensing
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915673199869952 |
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| author | Sun, Xin Ma, Rongjun Wei, Shu Cesar, Pablo Bosch, Jos A. Ali, Abdallah El |
| author_facet | Sun, Xin Ma, Rongjun Wei, Shu Cesar, Pablo Bosch, Jos A. Ali, Abdallah El |
| contents | 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) $\times$ 2 (label: Human vs. AI) $\times$ 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_12348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Trust Toward Human versus AI-generated Health Information through Behavioral and Physiological Sensing Sun, Xin Ma, Rongjun Wei, Shu Cesar, Pablo Bosch, Jos A. Ali, Abdallah El Human-Computer Interaction 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) $\times$ 2 (label: Human vs. AI) $\times$ 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. |
| title | Understanding Trust Toward Human versus AI-generated Health Information through Behavioral and Physiological Sensing |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.12348 |