  Machine-learning models have demonstrated great success in learning complex
patterns that enable them to make predictions about unobserved data. In
addition to using models for prediction, the ability to interpret what a model
has learned is receiving an increasing amount of attention. However, this
increased focus has led to considerable confusion about the notion of
interpretability. In particular, it is unclear how the wide array of proposed
interpretation methods are related, and what common concepts can be used to
evaluate them.
  We aim to address these concerns by defining interpretability in the context
of machine learning and introducing the Predictive, Descriptive, Relevant (PDR)
framework for discussing interpretations. The PDR framework provides three
overarching desiderata for evaluation: predictive accuracy, descriptive
accuracy and relevancy, with relevancy judged relative to a human audience.
Moreover, to help manage the deluge of interpretation methods, we introduce a
categorization of existing techniques into model-based and post-hoc categories,
with sub-groups including sparsity, modularity and simulatability. To
demonstrate how practitioners can use the PDR framework to evaluate and
understand interpretations, we provide numerous real-world examples. These
examples highlight the often under-appreciated role played by human audiences
in discussions of interpretability. Finally, based on our framework, we discuss
limitations of existing methods and directions for future work. We hope that
this work will provide a common vocabulary that will make it easier for both
practitioners and researchers to discuss and choose from the full range of
interpretation methods.
