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We describe a stance towards the generation of explanations in AI agents thatis both human-centered and design-based. We collect questions about the workingof an AI agent through participatory design by focus groups. We capture anagent's design through a Task-Method-Knowledge model that explicitly specifiesthe agent's tasks and goals, as well as the mechanisms, knowledge andvocabulary it uses for accomplishing the tasks. We illustrate our approachthrough the generation of explanations in Skillsync, an AI agent that linkscompanies and colleges for worker upskilling and reskilling. In particular, weembed a question-answering agent called AskJill in Skillsync, where AskJillcontains a TMK model of Skillsync's design. AskJill presently answershuman-generated questions about Skillsync's tasks and vocabulary, and therebyhelps explain how it produces its recommendations.

Explanation as Question Answering based on a Task Model of the Agent's Design