讲座题目 | Navigating Workforce Transformation: AI Augmentation and Automation Risks Across Occupation Zones | ||
主讲人 （单位） | Ting Zhang （巴尔的摩大学） | 主持人 （单位） | 刘修岩、陈露 （东南大学） |
讲座时间 | 2025年3月20日（周四） 10:00 | 讲座地点 | 经管楼B201会议室 |
主讲人简介 |
Ting Zhang，博士、美国巴尔的摩大学梅里克商学院哈里·Y·赖特经济学讲席教授，并荣获马里兰大学系统董事会2023年学术/研究杰出教授奖以及2024年大学校长教授奖。张博士的研究领域包括创业学、老龄化、劳动力发展和数字化。她曾任北美区域科学委员会主席，兼任东北区域科学协会主席，马里兰州州长平等薪酬委员会成员、美国公共政策与管理协会（APPAM）创业政策导师、美国劳工部研究员以及考夫曼基金会博士论文奖学金获得者。张博士已出版多部书籍和著作。她主持了美国劳工部、农业部、教育部、社会保障管理局、比尔和梅琳达·盖茨基金会、州劳工部和社会服务部等机构超过1000万美元的研究经费的多个项目。她曾在哈佛大学、剑桥大学、美国国会联合经济委员会、小企业委员会以及美国国家新闻俱乐部等多次发表演讲。张博士是《Small Business Economics》等期刊的编委委员，《Journal of Urban Management》的副主编，《Entrepreneurship & Regional Development》等期刊的特邀主编。她的研究成果曾被《福布斯》、《时代》和《彭博商业周刊》等媒体关注。 | ||
讲座内容摘要 | Automation is rapidly reshaping the labor market, prompting critical questions about the future of work, job security, and the skills necessary to thrive in an automated world. This paper examines two distinct types of automation: computerized automation, driven by rule-based, repetitive tasks, and AI-driven automation, powered by large language models that enhance cognitive, non-routine, and creative. Understanding these distinctions is vital for workers preparing for the future of work. We explore how task similarities across occupations influence upskilling opportunities and the risk of displacement, highlighting a complex relationship between task similarity and automation risk, while AI-driven automation presents new opportunities for skill enhancement, particularly in knowledge-intensive roles. This study introduces a four-occupation zone framework based on AI Augmentation and Computerized Displacement, offering a more nuanced understanding of automation’s effects on various job sectors. By combining task analysis with upskilling patterns, the study provides deeper insights into how automation impacts labor market dynamics, informing policies to address worker displacement and skill development needs. The findings contribute to a better understanding of how workers can adapt to automation and how policymakers can design targeted reskilling initiatives. | ||
