The Eye of The Beholder: Asymmetric Perceptions of AI Misuse in Self Versus Others

As artificial intelligence (AI) applications proliferate across professions, understanding factors that influence individuals' perceived likelihood to misuse AI is critical. This research delved into the intricate dynamics of individual AI experiences and their planned behavioral attributes, including behavioral attitudes, social expectations, and behavioral intentions. Using Attribution Theory and the Theory of Planned Behavior, we investigated how these factors synergistically interacted to shape AI misuse perceptions. Our findings indicated that individuals with high AI familiarity believe that AI misuse was more pronounced for individuals outside their own business profession compared to those within it. However, this perception varied significantly across disciplines. Additionally, this asymmetric cross-functional perceived susceptibility for AI misuse was moderated by various cognitive and motivational attributions. This study contributed to the burgeoning field of AI ethics by offering a comprehensive analysis of the human-AI interface, paving the way for future research, informed interventions, and policies that foster responsible AI use.

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Publication Details

Journal
IEEE Engineering Management Review
Published
2025-08-12
DOI
https://doi.org/10.1109/emr.2025.3597984
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

The Eye of The Beholder: Asymmetric Perceptions of AI Misuse in Self Versus Others

Jinpei Wu, Qingyun Zhu, Barry A. Friedman, Yiru Wang
IEEE Engineering Management Review
Ethics and Social Impacts of AI
article

The Eye of The Beholder: Asymmetric Perceptions of AI Misuse in Self Versus Others

Jinpei Wu, Qingyun Zhu, Barry A. Friedman, Yiru Wang
article en

Abstract

As artificial intelligence (AI) applications proliferate across professions, understanding factors that influence individuals' perceived likelihood to misuse AI is critical. This research delved into the intricate dynamics of individual AI experiences and their planned behavioral attributes, including behavioral attitudes, social expectations, and behavioral intentions. Using Attribution Theory and the Theory of Planned Behavior, we investigated how these factors synergistically interacted to shape AI misuse perceptions. Our findings indicated that individuals with high AI familiarity believe that AI misuse was more pronounced for individuals outside their own business profession compared to those within it. However, this perception varied significantly across disciplines. Additionally, this asymmetric cross-functional perceived susceptibility for AI misuse was moderated by various cognitive and motivational attributions. This study contributed to the burgeoning field of AI ethics by offering a comprehensive analysis of the human-AI interface, paving the way for future research, informed interventions, and policies that foster responsible AI use.

IEEE Engineering Management ReviewVol. 54(1)
Central Connecticut State University (US), San Diego State University (US), State University of New York at Oswego (US)
Reduced inequalities
Openalex Percentile: Top 81%
Ethics and Social Impacts of AI
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