時間：4月9日上午9:00-10:30
騰訊會議號：342 825 772
報告內容簡介:
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.
報告人簡介：
黃彥博士，卡內基梅隆大學泰珀商學院助理教授。研究興趣在于使用定量方法來研究技術的經濟和社會影響，特別是人工智能、機器學習和基于人群的技術，以及它們背后的機制。基于這些理解，提出建議戰略和政策，促進生產和合理使用技術，并確定技術支持平臺和應用的有效設計。黃彥博士在清華大學獲得學士學位，在卡內基梅隆大學獲得博士學位。
（承辦：管理工程系、科研與學術交流中心）
