嘉宾简介：
谭寅亮博士是美国休斯顿大学鲍尔商学院决策和信息科学终身教授、鲍尔讲席教授，以及供应链管理方向系主任。他同时还担任鲍尔商学院人工智能研究中心副主任及经济学家，休斯顿大学慧与科技数据科学研究中心资深研究员。此前他在美国杜兰大学弗里曼商学院管理科学方向担任助理教授、副教授、戈德林国际教育中心行政主任，并获得终身教授与讲席教授职位。谭寅亮博士毕业于美国佛罗里达大学沃灵顿商学院，学习运营管理及信息系统。他拥有丰富的商业分析方面的教学经验，获得过弗里曼商学院年度最佳教师奖。其研究兴趣主要集中在数字经济，人工智能，以及科技管理与创新等领域。他在国际顶级期刊Management Science, MIS Quarterly, Information Systems Research, Production and Operations Management， Decision Science 等多次发表论文，并获得过国际决策科学年会的最佳论文奖。谭博士现在担任Production and Operations Management（国际顶级期刊）的资深编辑, Decision Sciences Journal的部门编辑, 以及Information & Management的副编辑。他于2019年被评为世界最佳40名40岁以下的商学院教授, 同年他获得了国际决策科学学会颁发的早期职业成就奖。他于2022年获得INFORMS 信息系统学会颁发的 Sandy Slaughter 早期职业成就奖以表彰他对信息系统领域做出的贡献。
讲座简介：
AI assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. Specifically, we investigate the impact of AI smartness—whether the AI assistant is empowered by machine learning intelligence—and AI transparency—whether physicians are informed of the assistant feature. We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, i.e., adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that AI smartness can increase the adoption rate and shorten the adoption timing, while AI transparency can only shorten the adoption timing. Moreover, the impact of transparency on the adoption rate is contingent on the smartness level of the assistant: AI transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the assistant is smart. Our study can guide platforms in designing their AI strategies. In particular, platforms should develop and apply smart AI algorithms in aiding physicians, and also keep physicians informed on such development and application, especially when the smartness level of the algorithms is low.
