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AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors

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  • Hanzhe Li
  • Jin Li
  • Ye Luo
  • Xiaowei Zhang

Abstract

This paper examines how AI persuades doctors when their diagnoses differ. Disagreements arise from two sources: attention differences, which are objective and play a complementary role to the doctor, and comprehension differences, which are subjective and act as substitutes. AI's interpretability influences how doctors attribute these sources and their willingness to change their minds. Surprisingly, uninterpretable AI can be more persuasive by allowing doctors to partially attribute disagreements to attention differences. This effect is stronger when doctors have low abnormality detection skills. Additionally, uninterpretable AI can improve diagnostic accuracy when doctors have career concerns.

Suggested Citation

  • Hanzhe Li & Jin Li & Ye Luo & Xiaowei Zhang, 2024. "AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors," Papers 2410.01114, arXiv.org.
    • Handle: RePEc:arx:papers:2410.01114
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    References listed on IDEAS

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    1. Victoria A. Shaffer & C. Adam Probst & Edgar C. Merkle & Hal R. Arkes & Mitchell A. Medow, 2013. "Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?," Medical Decision Making, , vol. 33(1), pages 108-118, January.
    2. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    3. Nikhil Agarwal & Alex Moehring & Pranav Rajpurkar & Tobias Salz, 2023. "Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology," NBER Working Papers 31422, National Bureau of Economic Research, Inc.
    4. David C Chan & Matthew Gentzkow & Chuan Yu, 2022. "Selection with Variation in Diagnostic Skill: Evidence from Radiologists [The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 729-783.
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