Working Paper
AI Persuasion, Bayesian Attribution, and Career Concern of Doctors
This version: September 2024
JEL: D02, D83, I10, I30, M16
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.
Related Research
Suggested Citation
Li, H., Li, J., Luo, Y., & Zhang, X. (2024). AI persuasion, Bayesian attribution, and career concerns of doctors (CAMO Working Paper No. 2024-01). HKU Centre for AI, Management and Organization. https://camo.hku.hk/ai-persuasion-bayesian-attribution-and-career-concern-of-doctors/
BibTeX
@techreport{li_li_luo_zhang_2024,
author = {Li, Hanzhe and Li, Jin and Luo, Ye and Zhang, Xiaowei},
title = {{AI} Persuasion, {B}ayesian Attribution, and Career Concerns of Doctors},
institution = {HKU Centre for AI, Management and Organization},
type = {{CAMO} Working Paper},
number = {2024-01},
year = {2024},
month = sep,
url = {https://camo.hku.hk/ai-persuasion-bayesian-attribution-and-career-concern-of-doctors/}
}