  We describe a stance towards the generation of explanations in AI agents that
is both human-centered and design-based. We collect questions about the working
of an AI agent through participatory design by focus groups. We capture an
agent's design through a Task-Method-Knowledge model that explicitly specifies
the agent's tasks and goals, as well as the mechanisms, knowledge and
vocabulary it uses for accomplishing the tasks. We illustrate our approach
through the generation of explanations in Skillsync, an AI agent that links
companies and colleges for worker upskilling and reskilling. In particular, we
embed a question-answering agent called AskJill in Skillsync, where AskJill
contains a TMK model of Skillsync's design. AskJill presently answers
human-generated questions about Skillsync's tasks and vocabulary, and thereby
helps explain how it produces its recommendations.
