We explore potential benefits of incorporating Rhetorical Design into the design of Explainable Artificial Intelligence (XAI) systems. While XAI is traditionally framed around explaining individual predictions or overall system behavior, explanations may also function as rhetorical arguments that shape how users evaluate a system's usefulness and credibility, and how they develop appropriate trust for adoption. In real-world, in-situ interactions, explanations can thus produce experiential and affective rhetorical effects that are not fully captured by traditional XAI design goals that focus primarily on how AI works. To address this gap, we propose Rhetorical XAI, which bridges two explanatory goals: how AI works and why AI merits use. Rhetorical XAI comprises three appeals in explanation design: logos, which aligns technical logic with human reasoning through visual and textual abstractions; ethos, which establishes contextual credibility based on the explanation source and its appropriateness to the decision task; and pathos, which engages user emotionally by framing explanations around their motivations, expectations, or situated needs during interaction. We conduct a narrative review synthesizing design strategies from prior XAI work aligned with these three rhetorical appeals, highlighting both opportunities and challenges of integrating rhetorical design into XAI.

翻译：本文探讨将修辞设计融入可解释人工智能系统设计的潜在优势。传统可解释人工智能主要聚焦于解释个体预测或整体系统行为，而解释本身亦可作为修辞论证手段，影响用户对系统实用性与可信度的评估，并促进形成适宜采纳的信任关系。在实际情境交互中，解释能够产生传统可解释人工智能设计目标（侧重于AI工作机制）未能完全涵盖的体验性与情感性修辞效果。为弥合此差距，我们提出修辞性可解释人工智能框架，该框架衔接两大解释目标：AI如何运作与AI为何值得使用。修辞性可解释人工智能包含解释设计中的三种诉求：逻辑诉求——通过视觉与文本抽象使技术逻辑与人类推理相协调；信誉诉求——基于解释来源及其与决策任务的适配性建立情境可信度；情感诉求——围绕用户在交互过程中的动机、预期或情境需求构建解释以引发情感共鸣。我们通过叙事性综述整合了既往可解释人工智能研究中符合这三种修辞诉求的设计策略，同时指出将修辞设计融入可解释人工智能所面临的机遇与挑战。