解释AI不能只靠技术，要设计好谁看、看什么、怎么看。
Beyond Technocratic XAI: The Who, What & How in Explanation Design
- 提出三步解释设计法：谁需要、解释什么、如何传达。
- 强调伦理风险，避免加剧认知不平等与责任模糊。
- 适合开发可解释系统的实践者和伦理审查人员。
可解释人工智能（XAI）提供了多种使复杂模型可理解的技术，但在实践中，生成有意义的解释是一项依赖上下文的任务，需要有意识的设计决策以确保可访问性和透明性。本文将解释重新定义为一种情境化的设计过程，特别适用于参与构建和部署可解释系统的从业者。基于先前研究和设计思维原则，我们提出了一个三部分的XAI解释设计框架：明确谁需要解释、需要解释什么内容、以及如何交付解释。同时强调伦理考量的重要性，包括认知不平等风险、强化社会不公以及掩盖问责制与治理的问题。通过将解释视为一种社会技术设计过程，该框架倡导一种注重上下文的XAI方法，支持有效沟通并推动伦理负责任的解释发展。
原文摘要 · Abstract (English)
The field of Explainable AI (XAI) offers a wide range of techniques for making complex models interpretable. Yet, in practice, generating meaningful explanations is a context-dependent task that requires intentional design choices to ensure accessibility and transparency. This paper reframes explanation as a situated design process -- an approach particularly relevant for practitioners involved in building and deploying explainable systems. Drawing on prior research and principles from design thinking, we propose a three-part framework for explanation design in XAI: asking Who needs the explanation, What they need explained, and How that explanation should be delivered. We also emphasize the need for ethical considerations, including risks of epistemic inequality, reinforcing social inequities, and obscuring accountability and governance. By treating explanation as a sociotechnical design process, this framework encourages a context-aware approach to XAI that supports effective communication and the development of ethically responsible explanations.
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