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.
翻译：我们描述了在以人为中心和基于设计为主的AI代理人中产生解释的立场。我们通过焦点小组的参与设计收集了有关AI代理人工作的问题。我们通过一个任务-方法-知识模型捕获了该代理人的设计,该模型明确规定了该代理人的任务和目标,以及该代理人为完成任务所使用的机制、知识和词汇。我们通过在Skitync中生成解释来说明我们的方法,该工具将公司和学院联系起来,以便工人提高技能和再培训。特别是,我们加入了一个名为AskJill in Skillync的问题解答代理,AskJill在Skyync中含有一个TMKSchync设计模型。问Jill目前回答人就Schillync的任务和词汇产生的问题,从而帮助解释它如何产生建议。
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