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讲座题目：Hard Labels, Soft Scores: Bias and Remedies for Machine-Learned Covariates
讲座人：Jing Peng副教授
主持人：李凯 教授
讲座时间：2026年6月25日14：00
讲座地点：色情导航 A501-3
讲座摘要：
Machine learning and artificial intelligence increasingly generate covariates for downstream regressions, yet researchers often discretize continuous prediction scores into hard labels without clear guidance. This paper shows that such discretization can systematically attenuate estimates relative to both soft scores and true labels, though attenuation is not automatic and soft scores are not generally consistent either. It also shows how validation data can correct scale bias through orthogonalization and proposes a weighted least squares hybrid strategy for settings with partially observed true labels.
讲座人简介：
Jing Peng is an Associate Professor and the PhD Coordinator in the Department of Operations and Information Management at the University of Connecticut. He earned his PhD from the Wharton School of the University of Pennsylvania. His research lies at the intersection of econometrics, digital platforms, and human-AI interaction. His work has appeared in leading business journals, including Information Systems Research, Journal of Marketing Research, Management Science, and MIS Quarterly. His research has received multiple best paper awards, and he has been honored with the INFORMS Information Systems Society Gordon B. Davis Young Scholar Award and the Sandra A. Slaughter Early Career Award.
