  Despite increasing interest in the field of Interpretable Machine Learning
(IML), a significant gap persists between the technical objectives targeted by
researchers' methods and the high-level goals of consumers' use cases. In this
work, we synthesize foundational work on IML methods and evaluation into an
actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap
between researchers and consumers, illustrated by the lack of connections
between its methods and use cases components. It also provides the foundation
from which we describe a three-step workflow to better enable researchers and
consumers to work together to discover what types of methods are useful for
what use cases. Eventually, by building on the results generated from this
workflow, a more complete version of the taxonomy will increasingly allow
consumers to find relevant methods for their target use cases and researchers
to identify applicable use cases for their proposed methods.
