主讲人

（单位）

黄庭亮

（田纳西大学）

主持人

（单位）

李四杰，陈静

讲座时间

2026年6月9日（周二）下午15:00

讲座地点

经管楼B201

主讲人简介

Tingliang Huang is the inaugural Amazon Distinguished Professor of Business Analytics at the Haslam College of Business, University of Tennessee (UT), the Business Analytics PhD Program Recruiting Lead, and Honorary Professor at UCL School of Management, University College London (UCL), UK. Before joining UT, he was tenured at Boston College Carroll School of Management and the William S. McKiernan ’78 Family Faculty Fellow. His current research focuses on AI and business analytics. His research articles have been published in top journals such as Manufacturing & Service Operations Management (M&SOM), Marketing Science, and Management Science. He has won various research & teaching awards including the 2025 Vallett Family Outstanding Researcher Award, the 2023 INFORMS Workshop on Data Science Best Paper Award, 2018 POMS Wickham Skinner Early Career Research Accomplishments Award, the 2018 Most Influential Paper Award in Service Operations, the 2015 Wickham Skinner Best Paper Award, the Teaching Star Award. He was recognized by the Management Science and M&SOM Meritorious Service Awards six times for his exceptional services to these journals. He is Deputy Editor for Service Science, Associate or Senior Editor for M&SOM, POM, Decision Sciences, Naval Research Logistics, and IISE Transactions. He obtained his PhD from the Kellogg School of Management, Northwestern University, MS from University of Minnesota, and BS from USTC.

讲座内容摘要

Artificial Intelligence (AI)'s impact on work is important but ambiguous. We first present a conceptual framework that considers the two dimensions of AI's job content and job opportunity effects. We develop a novel occupational AI exposure measure using a sentence transformer model to compare the semantic similarity between the occupation descriptions and AI patents. We find that, on average, occupations with higher AI exposure experience a decrease in the importance of a wide range of work activities, coupled with an increase in job opportunities. In addition, we observe important heterogeneity in AI's work activity and job opportunity effects across occupations with different education requirements. We further study how the application of predictive AI or machine learning (ML) changes job requirements for workers. We conceptualize machine learning as "expertise-biased" technological change. To test this proposition, we develop two empirically testable hypotheses based on how expertise is (1) developed through prior work experience and (2) applied in higher-order tasks. Our empirical analyses employ over 51 million job postings and we find that firms utilizing ML technologies also raise their job requirements for prior work experience and skills, especially those related to higher-order tasks such as decision-making and problem-solving. These effects are evident not only for knowledge workers but also for roles that typically do not require a college education. Furthermore, these effects are especially pronounced in occupations characterized by high skill turnover and non-routine work. These findings demonstrate how machine learning utilization within the firm may have changed the skill composition of workers and hence reshaping the nature of work at scale.