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Understanding Risk Preference and Risk Perception When Adopting High-Risk and Low-Risk AI Technologies.

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Title: Understanding Risk Preference and Risk Perception When Adopting High-Risk and Low-Risk AI Technologies.
Authors: Wei, Mengyi (AUTHOR), Zhou, Kyrie Zhixuan (AUTHOR), Chen, Dongsheng (AUTHOR), Sanfilippo, Madelyn Rose (AUTHOR), Zhang, Puzhen (AUTHOR), Chen, Chuan (AUTHOR), Feng, Yu (AUTHOR), Meng, Liqiu (AUTHOR)
Source: International Journal of Human-Computer Interaction. Dec2025, Vol. 41 Issue 24, p15295-15310. 16p.
Subjects: Risk perception, Artificial intelligence, Policy analysis, Socioeconomic factors, Risk aversion
Abstract: Recent advances in AI have significantly changed people's lives, yet sometimes their inherent risks deter adoption. Risk preference and perception in AI remain understudied. We surveyed 406 participants to explore how risk preferences, risk perceptions, and socioeconomic variables influence AI adoption in high-risk (autonomous vehicles) and low-risk (recommendation algorithms) contexts. Socioeconomic groups overall show different levels of risk aversion and seeking across scenarios. For high-risk autonomous driving, the risk aspects tend to be centralized. In contrast, the risk aspects of recommendation algorithms are more dispersed. These findings indicate a prevailing inclination among individuals toward caution regarding risks, highlighting the need for government policies that distinguish high- and low-risk AI. Regulations for autonomous vehicles should be strengthened to ensure safety and clarify liability, while those for recommendation algorithms should be expanded to enhance public risk awareness. This study aims to support policymakers toward more targeted AI risk management. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Understanding Risk Preference and Risk Perception When Adopting High-Risk and Low-Risk AI Technologies.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Dec2025, Vol. 41 Issue 24, p15295-15310. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Risk+perception%22">Risk perception</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Policy+analysis%22">Policy analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+factors%22">Socioeconomic factors</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+aversion%22">Risk aversion</searchLink>
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  Label: Abstract
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  Data: Recent advances in AI have significantly changed people's lives, yet sometimes their inherent risks deter adoption. Risk preference and perception in AI remain understudied. We surveyed 406 participants to explore how risk preferences, risk perceptions, and socioeconomic variables influence AI adoption in high-risk (autonomous vehicles) and low-risk (recommendation algorithms) contexts. Socioeconomic groups overall show different levels of risk aversion and seeking across scenarios. For high-risk autonomous driving, the risk aspects tend to be centralized. In contrast, the risk aspects of recommendation algorithms are more dispersed. These findings indicate a prevailing inclination among individuals toward caution regarding risks, highlighting the need for government policies that distinguish high- and low-risk AI. Regulations for autonomous vehicles should be strengthened to ensure safety and clarify liability, while those for recommendation algorithms should be expanded to enhance public risk awareness. This study aims to support policymakers toward more targeted AI risk management. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/10447318.2025.2495844
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Artificial intelligence
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      – SubjectFull: Policy analysis
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      – SubjectFull: Socioeconomic factors
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      – SubjectFull: Risk aversion
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              Text: Dec2025
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