  The growing use of machine learning models in consequential settings has
highlighted an important and seemingly irreconcilable tension between
transparency and vulnerability to gaming. While this has sparked sizable debate
in legal literature, there has been comparatively less technical study of this
contention. In this work, we propose a clean-cut formulation of this tension
and a way to make the tradeoff between transparency and gaming. We identify the
source of gaming as being points close to the \emph{decision boundary} of the
model. And we initiate an investigation on how to provide example-based
explanations that are expansive and yet consistent with a version space that is
sufficiently uncertain with respect to the boundary points' labels. Finally, we
furnish our theoretical results with empirical investigations of this tradeoff
on real-world datasets.
