  To ensure accountability and mitigate harm, it is critical that diverse
stakeholders can interrogate black-box automated systems and find information
that is understandable, relevant, and useful to them. In this paper, we eschew
prior expertise- and role-based categorizations of interpretability
stakeholders in favor of a more granular framework that decouples stakeholders'
knowledge from their interpretability needs. We characterize stakeholders by
their formal, instrumental, and personal knowledge and how it manifests in the
contexts of machine learning, the data domain, and the general milieu. We
additionally distill a hierarchical typology of stakeholder needs that
distinguishes higher-level domain goals from lower-level interpretability
tasks. In assessing the descriptive, evaluative, and generative powers of our
framework, we find our more nuanced treatment of stakeholders reveals gaps and
opportunities in the interpretability literature, adds precision to the design
and comparison of user studies, and facilitates a more reflexive approach to
conducting this research.
