时间：4月9日上午9:00-10:30

腾讯会议号：342 825 772

报告内容简介:

Should firms that use machine learning algorithms for decision-making make their algorithms transparent? Despite growing calls for algorithmic transparency, most firms have kept their algorithms opaque, citing potential gaming by users that may negatively affect the algorithms' predictive power. We develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent benefits the firm. We show that, in some cases, even the predictive power of the algorithm may increase if the firm makes the algorithm transparent. By contrast, users may not always be better off under algorithmic transparency. The results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve on them is close to zero. Overall, our results show that firms should not view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.

报告人简介：

黄彦博士，卡内基梅隆大学泰珀商学院助理教授。研究兴趣在于使用定量方法来研究技术的经济和社会影响，特别是人工智能、机器学习和基于人群的技术，以及它们背后的机制。基于这些理解，提出建议战略和政策，促进生产和合理使用技术，并确定技术支持平台和应用的有效设计。黄彦博士在清华大学获得学士学位，在卡内基梅隆大学获得博士学位。

（承办：管理工程系、科研与学术交流中心）