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Moral Judgments in the Age of Artificial Intelligence

Author

Listed:
  • Yulia W. Sullivan

    (Baylor University)

  • Samuel Fosso Wamba

    (TBS Education)

Abstract

The current research aims to answer the following question: “who will be held responsible for harm involving an artificial intelligence (AI) system?” Drawing upon the literature on moral judgments, we assert that when people perceive an AI system’s action as causing harm to others, they will assign blame to different entity groups involved in an AI’s life cycle, including the company, the developer team, and even the AI system itself, especially when such harm is perceived to be intentional. Drawing upon the theory of mind perception, we hypothesized that two dimensions of mind: perceived agency—attributing intention, reasoning, pursuing goals, and communicating to AI, and perceived experience—attributing emotional states, such as feeling pain and pleasure, personality, and consciousness to AI—mediated the relationship between perceived intentional harm and blame judgments toward AI. We also predicted that people are likely to attribute higher mind characteristics to AI when harm is perceived to be directed to humans than when it is perceived to be directed to non-humans. We tested our research model in three experiments. In all experiments, we found that perceived intentional harm led to blame judgments toward AI. In two experiments, we found perceived experience, not agency, mediated the relationship between perceived intentional harm and blame judgments. We also found that companies and developers were held responsible for moral violations involving AI, with developers received the most blame among the entities involved. Our third experiment reconciles the findings by showing that perceived intentional harm directed to a non-human entity did not lead to increased attributions of mind to AI. These findings have implications for theory and practice concerning unethical outcomes and behavior associated with AI use.

Suggested Citation

  • Yulia W. Sullivan & Samuel Fosso Wamba, 2022. "Moral Judgments in the Age of Artificial Intelligence," Journal of Business Ethics, Springer, vol. 178(4), pages 917-943, July.
    • Handle: RePEc:kap:jbuset:v:178:y:2022:i:4:d:10.1007_s10551-022-05053-w
    DOI: 10.1007/s10551-022-05053-w
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    References listed on IDEAS

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    1. Rai, Tage S. & Diermeier, Daniel, 2015. "Corporations are Cyborgs: Organizations elicit anger but not sympathy when they can think but cannot feel," Organizational Behavior and Human Decision Processes, Elsevier, vol. 126(C), pages 18-26.
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    Cited by:

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    3. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    4. Li, Tian-Ge & Zhang, Chu-Bing & Chang, Ying & Zheng, Wei, 2024. "The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    5. Michael Haenlein & Ming-Hui Huang & Andreas Kaplan, 2022. "Guest Editorial: Business Ethics in the Era of Artificial Intelligence," Journal of Business Ethics, Springer, vol. 178(4), pages 867-869, July.
    6. Muhammad Umair Shah & Umair Rehman & Bidhan Parmar & Inara Ismail, 2024. "Effects of Moral Violation on Algorithmic Transparency: An Empirical Investigation," Journal of Business Ethics, Springer, vol. 193(1), pages 19-34, August.
    7. Zhao, Taiyang & Ran, Yaxuan & Wu, Banggang & Lynette Wang, Valerie & Zhou, Liying & Lu Wang, Cheng, 2024. "Virtual versus human: Unraveling consumer reactions to service failures through influencer types," Journal of Business Research, Elsevier, vol. 178(C).
    8. Lukas Lanz & Roman Briker & Fabiola H. Gerpott, 2024. "Employees Adhere More to Unethical Instructions from Human Than AI Supervisors: Complementing Experimental Evidence with Machine Learning," Journal of Business Ethics, Springer, vol. 189(3), pages 625-646, January.

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