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Treating differently or equally: A study exploring attitudes towards AI moral advisors

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  • Liu, Yiming
  • Wang, Tianhong

Abstract

Artificial intelligence (AI) technology has evolved from serving primarily as a decision-maker in the past to increasingly taking on the role of an advisor. However, contemporary attitudes toward AI applications in moral decision-making remain unclear. In Study 1, we explored whether there is a difference in attitudes towards human and AI moral advisors when both are defined in the context of a one-time decision-making scenario. Studies 2a and 2b, with the goal of achieving higher ecological validity, optimized decision-making methods and scenarios, respectively. We obtained consistent results, indicating that people equally trust the advice of AI moral advisors and human moral advisors. When it comes to assigning responsibility after a decision, individuals assign responsibility equally to both AI and human advisors.

Suggested Citation

  • Liu, Yiming & Wang, Tianhong, 2025. "Treating differently or equally: A study exploring attitudes towards AI moral advisors," Technology in Society, Elsevier, vol. 82(C).
  • Handle: RePEc:eee:teinso:v:82:y:2025:i:c:s0160791x25000521
    DOI: 10.1016/j.techsoc.2025.102862
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    References listed on IDEAS

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    1. Wang, Shaofeng & Zhang, Hao, 2024. "Green entrepreneurship success in the age of generative artificial intelligence: The interplay of technology adoption, knowledge management, and government support," Technology in Society, Elsevier, vol. 79(C).
    2. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    3. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    4. Ivanov, Stanislav & Webster, Craig, 2024. "Automated decision-making: Hoteliers’ perceptions," Technology in Society, Elsevier, vol. 76(C).
    5. Andersen, Jens Peter & Degn, Lise & Fishberg, Rachel & Graversen, Ebbe K. & Horbach, Serge P.J.M. & Schmidt, Evanthia Kalpazidou & Schneider, Jesper W. & Sørensen, Mads P., 2025. "Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions," Technology in Society, Elsevier, vol. 81(C).
    6. Li, Jian & Huang, Jin-Song, 2020. "Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory," Technology in Society, Elsevier, vol. 63(C).
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