Algorithmic Transparency with Strategic Users

Abstract

Should firms that apply machine learning algorithms in their decision making make their algorithms transparent? Despite increasing calls for algorithmic transparency, most firms have kept their algorithms opaque citing potential gaming by users that may negatively affect the algorithm’s predictive power. We introduce an analytical model to investigate the issue of algorithmic transparency in the presence of strategic users from a firm’s perspective and present novel insights. Counter-intuitively, we show that the predictive power of ML algorithms may increase if the firm were to make them transparent. We identify a broad set of conditions under which making the algorithm transparent benefits the firm in terms of higher accuracy. The results hold even when the predictive power of the opaque algorithm comes mainly from correlational features and the cost for users to improve on them is close to zero.

Keywords: Algorithmic Transparency, Game Theory, Machine Learning, Strategic Classification, Signaling Game, Information Game

JEL Classification: M30, M31, M37, M38, M21, M15