Margin-distancing for safe model explanation

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 intense 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 -- it need not be one or the other. We identify the source of gaming as being points in the \emph{margin} 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 margin points' labels. Finally, we furnish our theoretical results with empirical investigations of this tradeoff on real-world datasets.