TIME
2024年6月11日 10：00 - 12：00
VENUE
信管学院308会议室
SPEAKER
Dr. Meizi Zhou is an Assistant Professor in the Information Systems department at Questrom School of Business, Boston University. Her research focuses on algorithmic and economic aspects of IT-enabled platforms in the areas of recommender systems and healthcare markets. Meizi’s research appears in Information Systems Research. Her studies have won Best Paper Award at the 14th Annual Conference on Health IT and Analytics (CHITA 2024), INFORMS Information Systems Society (ISS) Nunamaker-Chen Dissertation Award Winner 2023, Best Paper Award at ZEW Conference 2021 and Best Student Paper Award at INFORMS Workshop on Data Science 2020.
Before joining Questrom, Meizi obtained Ph.D. from the Department of Information and Decision Sciences at Carlson School of Management, University of Minnesota. And she received master's degree at Chinese Academy of Sciences, and bachelor’s degree at Renmin University of China. She has worked as research intern at companies such as Best Buy, JD.com, and Tencent.
TITLE
Can AI Distort Human Capital?
ABSTRACT
We document that interactions with manipulated AI can distort the development of human capital in opioid prescription contexts. Physicians in our sample adopted electronic health record software from a list of federally certified companies in 2011. Between 2016 and spring 2019, one company secretly embedded a biased AI reminder system to promote extended-release opioid sales. Affected physicians not only increased opioid claims relative to the control group during the treatment window but also maintained a higher propensity for prescriptions even after the removal of the biased function. This long-term distortion of human capital relies on the unconsciousness of AI biases and does not occur following other explicit promotions, such as pharmaceutical detailing payments. Using machine-learning algorithms, we quantify that human capital distortion explains 54% of the treatment effects in a physician decision model with dynamic learning. Experience with opioids, along with caution regarding elder patients, mitigates the distortion.
