  In this paper, we take a human-centered approach to interpretable machine
learning. First, drawing inspiration from the study of explanation in
philosophy, cognitive science, and the social sciences, we propose a list of
design principles for machine-generated explanations that are meaningful to
humans. Using the concept of weight of evidence from information theory, we
develop a method for producing explanations that adhere to these principles. We
show that this method can be adapted to handle high-dimensional, multi-class
settings, yielding a flexible meta-algorithm for generating explanations. We
demonstrate that these explanations can be estimated accurately from finite
samples and are robust to small perturbations of the inputs. We also evaluate
our method through a qualitative user study with machine learning
practitioners, where we observe that the resulting explanations are usable
despite some participants struggling with background concepts like prior class
probabilities. Finally, we conclude by surfacing design implications for
interpretability tools
