As AI systems become increasingly conversational, a gap emerges wherein explanations are studied as static artifacts, yet in practice, are experienced as dialogue. In this provocation, we argue that the conversational layer around an explanation is not incidental to its effectiveness, but a critical constituent. Drawing on three illustrative scenarios, we invite the CUI community to study explanations as interactive, conversational exchanges shaped by timing, tone, persona and conversational history, and introduce our vision for Human-Centered Conversational XAI (HC2XAI).

翻译：随着人工智能系统日益具备对话能力，一个差距逐渐显现：解释被作为静态产物来研究，而在实践中却被体验为对话。在这篇探讨性文章中，我们认为围绕解释的对话层并非对其有效性可有可无，而是其关键组成部分。通过三个说明性场景，我们邀请CUI社区将解释研究为受时机、语气、角色和对话历史影响的互动式对话交流，并介绍我们对以人为本的对话式可解释人工智能（HC2XAI）的愿景。