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When AI doctors lie about diagnosis: The effects of varying degrees of prosocial lies in patient–AI interactions

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  • Mao, Yuanyi
  • Hu, Bo
  • Kim, Ki Joon

Abstract

Instead of telling the whole truth, doctors sometimes resort to prosocial lies when diagnosing illness to protect patients from psychological harm. Recent advances in artificial intelligence (AI) have introduced AI doctors capable of telling prosocial lies in a medical setting. Accordingly, this study conducted a 3 (full truth vs. partial prosocial lie vs. full prosocial lie) × 2 (AI vs. human doctor) between-subjects experiment to examine how varying degrees of prosocial lying by different types of doctors are perceived by laypeople. The results showed that the full truth and partial prosocial lies elicited a similar level of acceptance, whereas full prosocial lies were the least acceptable. The effects of prosocial lying were mediated by autonomy violation and psychological benefits. Additionally, individuals preferred the truth from a human doctor rather than an AI doctor, but full prosocial lies were more acceptable from an AI doctor than from a human doctor.

Suggested Citation

  • Mao, Yuanyi & Hu, Bo & Kim, Ki Joon, 2024. "When AI doctors lie about diagnosis: The effects of varying degrees of prosocial lies in patient–AI interactions," Technology in Society, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:teinso:v:76:y:2024:i:c:s0160791x24000095
    DOI: 10.1016/j.techsoc.2024.102461
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    References listed on IDEAS

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    1. 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.
    2. Gneezy, Uri & Rockenbach, Bettina & Serra-Garcia, Marta, 2013. "Measuring lying aversion," Journal of Economic Behavior & Organization, Elsevier, vol. 93(C), pages 293-300.
    3. Uri Gneezy & Agne Kajackaite & Joel Sobel, 2018. "Lying Aversion and the Size of the Lie," American Economic Review, American Economic Association, vol. 108(2), pages 419-453, February.
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    Cited by:

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    3. Li, Jian & Zhao, Jingdi & Huang, Jinsong, 2025. "Social avoidance needs boost AI's nonsocial attribute valuation in secret consumption," Technology in Society, Elsevier, vol. 81(C).

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