  We addressed the problem of a lack of semantic representation for
user-centric explanations and different explanation types in our Explanation
Ontology (https://purl.org/heals/eo). Such a representation is increasingly
necessary as explainability has become an important problem in Artificial
Intelligence with the emergence of complex methods and an uptake in
high-precision and user-facing settings. In this submission, we provide
step-by-step guidance for system designers to utilize our ontology, introduced
in our resource track paper, to plan and model for explanations during the
design of their Artificial Intelligence systems. We also provide a detailed
example with our utilization of this guidance in a clinical setting.
