  Should firms that apply machine learning algorithms in their decision-making
make their algorithms transparent to the users they affect? Despite growing
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 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 machine learning
algorithms may increase if the firm makes them 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.
