Algorithmic Transparency with Strategic Users

Qiaochu Wang (), Yan Huang (), Stefanus Jasin () and Param Vir Singh ()
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Qiaochu Wang: Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Yan Huang: Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Stefanus Jasin: University of Michigan, Ann Arbor, Michigan 48109
Param Vir Singh: Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Management Science, 2023, vol. 69, issue 4, 2297-2317

Abstract: 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 algorithm can increase if the firm makes the algorithm transparent. By contrast, users may not always be better off under algorithmic transparency. These results hold even when the predictive power of the opaque algorithm comes 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 to motivate users to invest in more desirable features.

Keywords: algorithmic transparency; game theory; machine learning; strategic classification; signaling game (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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