  This work proposes xGEMs or manifold guided exemplars, a framework to
understand black-box classifier behavior by exploring the landscape of the
underlying data manifold as data points cross decision boundaries. To do so, we
train an unsupervised implicit generative model -- treated as a proxy to the
data manifold. We summarize black-box model behavior quantitatively by
perturbing data samples along the manifold. We demonstrate xGEMs' ability to
detect and quantify bias in model learning and also for understanding the
changes in model behavior as training progresses.
