2025年7月23日（周三）

10:30 - 12:00

顾彬

波士顿大学

Despite AI’s proven decision-making performance, adoption remains low in uncertain, repetitive business environments. Decision-makers often doubt optimal AI recommendations when short-term outcomes suffer from randomness (e.g., supply or demand fluctuations). This randomness leads to a cycle of distrust in the optimal AI recommendations, as users increasingly discount AI advice after observing poor short-term results. Two proposed solutions to increase AI adoption are providing deductive reasoning through explanations or inductive reasoning through ‘side-by-side’ performance benchmarking. While empirical studies demonstrate that these solutions can improve reliance and trust, most focus on tasks evaluated solely by the AI’s prediction accuracy. However, accuracy proves insufficient in uncertain environments when errors impact objectives differently. Such everyday decision environments create a critical gap in understanding how these solutions affect AI adoption in repetitive managerial decision-making.

顾彬教授是波士顿大学凯斯特罗姆商学院信息系统的Everett W. Lord杰出学者、教授兼系主任。

顾教授的研究兴趣主要集中在利用信息技术和人工智能来解决商业和社会中的信息不对称和社会公平问题。他更具体地研究了金融科技、数字平台、未来工作形态、在线社交媒体、社交网络和在线零售中的信息不对称和社会公平问题。他的研究成果发表在Management Science、MIS Quarterly、Information Systems Research、Journal of Management Information Systems等领先的商业学术期刊上，并获得了超过16000次引用。顾教授因其对信息系统学科的杰出学术贡献，于2022年荣获INFORMS Information Systems Society的Distinguished Fellow Award。

顾教授在宾夕法尼亚大学沃顿商学院获得了博士学位和硕士学位，同时在上海交通大学获得了国际贸易与计算机科学专业的学士学位。在加入波士顿大学之前，顾教授曾担任亚利桑那州立大学W.P.凯瑞商学院的Gladys Davis杰出教授以及中国事务副院长。他还曾在上海高级金融研究院（兼职）和德克萨斯大学奥斯汀分校任教。在进入学术界之前，顾教授曾在安达信会计师事务所（Arthur Andersen）担任顾问。