Algorithmic Transparency with Strategic Users

• 成果类型: Article
• 署名作者: Wang, Qiaochu; Huang, Yan; Jasin, Stefanus; Singh, Param Vir
• 署名单位: Carnegie Mellon University; University of Michigan System; University of Michigan
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4475
• 发表日期: 2023
• 页码: 2297-2317
• 关键词: algorithmic transparency; game theory; Machine Learning; strategic classification; signaling game

摘要

Should firms that apply machine learning algorithms in their decision making make their algorithms transparent to the users they affect? Despite the growing calls for algorithmic transparency, most firms keep their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm's predictive power. In this paper, 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 actually benefits the firm. We show that, in some cases, even the predictive power of the algorithmcan increase if the firm makes the algorithm transparent. By contrast, users may not always be better off under algorithmic transparency. These results hold evenwhen the predictive power of the opaque algorithmcomes largely from correlational features and the cost for users to improve them is minimal. We show that these insights are robust under several extensions of the main model. Overall, our results show that firms should not always view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever tomotivate users to invest inmore desirable features.