  The challenge of creating interpretable models has been taken up by two main
research communities: ML researchers primarily focused on lower-level
explainability methods that suit the needs of engineers, and HCI researchers
who have more heavily emphasized user-centered approaches often based on
participatory design methods. This paper reviews how these communities have
evaluated interpretability, identifying overlaps and semantic misalignments. We
propose moving towards a unified framework of evaluation criteria and lay the
groundwork for such a framework by articulating the relationships between
existing criteria. We argue that explanations serve as mediators between models
and stakeholders, whether for intrinsically interpretable models or opaque
black-box models analyzed via post-hoc techniques. We further argue that useful
explanations require both faithfulness and intelligibility. Explanation
plausibility is a prerequisite for intelligibility, while stability is a
prerequisite for explanation faithfulness. We illustrate these criteria, as
well as specific evaluation methods, using examples from an ongoing study of an
interpretable neural network for predicting a particular learner behavior.
