  Ante-hoc interpretability has become the holy grail of explainable artificial
intelligence for high-stakes domains such as healthcare; however, this notion
is elusive, lacks a widely-accepted definition and depends on the operational
context. It can refer to predictive models whose structure adheres to
domain-specific constraints, or ones that are inherently transparent. The
latter conceptualisation assumes observers who judge this quality, whereas the
former presupposes them to have technical and domain expertise (thus alienating
other groups of explainees). Additionally, the distinction between ante-hoc
interpretability and the less desirable post-hoc explainability, which refers
to methods that construct a separate explanatory model, is vague given that
transparent predictive models may still require (post-)processing to yield
suitable explanatory insights. Ante-hoc interpretability is thus an overloaded
concept that comprises a range of implicit properties, which we unpack in this
paper to better understand what is needed for its safe adoption across
high-stakes domains. To this end, we outline modelling and explaining
desiderata that allow us to navigate its distinct realisations in view of the
envisaged application and audience.
