Trusting the Middle in the TNO Trust Task

How Medium Risk and Reliability Shape AI‐Assisted Advice‐Taking

Bibliographic Data

ID23039333
AuthorsZaoyi Sun (0000-0002-4551-3606, Zhejiang University of Technology, corresponding author), Jiaxin Ding (0000-0001-6250-9740, East China Normal University)
EditorsMahmaod Alrawad (0000-0002-8871-3392)
Year2025
Volume2025
Issue1
Publication date2025-01-01
Open AccessYes
TypeARTICLE
VenueHuman Behavior and Emerging Technologies (JOURNAL)
Journal identifiersISSN: 2578-1863 • E-ISSN: 2578-1863
PublisherWiley (PUBLISHER • GB)
DOI10.1155/hbe2/6901297
OpenAlexW4416533234
LanguageEN
References cited94

As artificial intelligence (AI) becomes increasingly integrated into decision‐making processes, understanding how people trust and rely on AI versus human experts is essential. In particular, the complexities of medium‐level task risk and advice reliability require further exploration. Using the judge–advisor system (JAS) model, this study investigates the effects of advice source (human vs. AI), advice reliability (low, medium, and high), and task risk (low, medium, and high) on subjective trust, advice reliance, and eye‐tracking patterns. Results show that medium‐risk tasks elicited the highest subjective trust but the lowest attention to advice, while reliance and final fixations were higher than in low‐risk tasks. Importantly, trust in AI relative to human experts depended on both reliability and risk: AI was favored in medium‐risk, medium‐reliability conditions but was trusted less than humans in high‐risk contexts. These findings highlight the often‐overlooked role of medium levels of risk and reliability in human–AI collaboration, offering insights for optimizing AI‐assisted decision‐making.

Advice (programming) · Human reliability · Reliability (semiconductor) · Risk assessment · Task (project management) · Decision-Making and Behavioral Economics · Ethics and Social Impacts of AI · Human-Automation Interaction and Safety

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