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. In this talk, Yan Huang from Tepper School of Business of Carnegie Mellon University discusses their development of an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights.

This talk was presented at the Harvard Business School Crossing Disciplines: Studying Fairness, Bias, and Inequality in Management and Decision Sciences Research workshop on May 21, 2021.

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