  Explainability is one of the key ethical concepts in the design of AI
systems. However, attempts to operationalize this concept thus far have tended
to focus on approaches such as new software for model interpretability or
guidelines with checklists. Rarely do existing tools and guidance incentivize
the designers of AI systems to think critically and strategically about the
role of explanations in their systems. We present a set of case studies of a
hypothetical AI-enabled product, which serves as a pedagogical tool to empower
product designers, developers, students, and educators to develop a holistic
explainability strategy for their own products.
