信息管理与商业智能系学术讲座

时间：2024年6月6日14:00-15:30

线下：思源楼524室

线上：腾讯会议室号622288219/密码722722

题目：发掘人工智能对科研、教育和社会的影响

主讲人：安若鹏教授

主持人：张诚信息管理与商业智能系教授

讲座简介:

安若鹏博士的讲座，通过大量实践案例，对充分发掘人工智能在科研、教育和社会的影响进行深入探讨。在科研领域，安博士将详细阐述如何通过人工智能打击虚假信息、提升精准营养、实时辅助政策制定、推进疾病监测和诊断、革新研究工具和方法以及避免算法偏见。在教育领域，安博士将介绍人工智能工作坊、全校人工智能课程、以及面向全球的华大人工智能高级证书项目。在社会影响方面，安博士将分享人工智能在社区推广、提升社交媒体影响力以及开发促进公共健康和社会工作实践的网络和移动应用方面的见解。这场讲座不仅是对人工智能影响的概述，更是深度展示其在实践中全方位应用的盛宴。

嘉宾简介:

安若鹏，现任圣路易斯华盛顿大学 (Washington University in St. Louis) 布朗学院 (Brown School) 及计算与数据科学部 (Division of Computational & Data Sciences) 终身副教授 (Tenured Associate Professor)、博士生导师、布朗学院数据科学负责人 (Faculty Lead in Data Science)、校长办公室Faculty Fellow in AI Innovations for Education、美国国立卫生院 (NIH) 糖尿病转化研究中心 (Center for Diabetes Translation Research) 人工智能大数据部主任。2018年当选为美国流行病学学院 (American College of Epidemiology) 院士。2023年当选为美国健康行为学院 (American Academy of Health Behavior) 院士。其研究得到美国联邦政府和企业（如OpenAI、雅培、安进）资助。

迄今在国际同行评审期刊上发表论文逾210篇，影响因子 (Impact Factor) 总计逾800。为逾120个国际学术期刊提供同行评审，因相关研究累计接受美国和国际媒体采访报道逾百次，如：时代周刊 (TIME)、纽约时报 (New York Times)、福布斯 (Forbes)、路透社 (Reuters)、福克斯 (Fox)、CNN、华盛顿邮报 (Washington Post)、洛杉矶时报 (Los Angeles Times) 等。

2015年论文被时代周刊列入该年度健康领域最重要的100个发现。2018年接受国际卫生组织 (World Health Organization) 邀请, 担任在日内瓦举行的首届全球大气污染与健康大会 (1st WHO Global Conference on Air Pollution and Health) 报告的主笔人之一。连续多年被评为大学杰出教师 (List of Teachers Ranked As Excellent By Their Students)。入选爱思唯尔 (Elsevier) 全球前2%顶尖科学家榜单 (World’s Top 2% Scientists)。

时间：2024年6月12日 10:00-11:30

线上：腾讯会议室号789120547/密码722722

线下：李达三楼104室

题目：Can AI Distort Human Capital?

主讲人：Meizi Zhou（周美孜）波士顿大学（Boston University）

主持人：刘子博信息管理与商业智能系青年副研究员

内容摘要:

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.

嘉宾简介:

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.