让AI解释更懂人：根据用户偏好动态调整说明内容。
Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations
- 用用户记忆模型判断哪些信息对当前用户是新知识。
- 基于用户历史互动，自动筛选最相关的内容进行解释。
- 适合需要个性化交互的AI助手、教育工具等场景。
AI解释的需求主要源于提升黑箱机器学习模型的透明度。然而，聚焦内部机制的解释往往不适合非专家用户。为实现以人为本的AI解释，代理应关注个体偏好与上下文。本文提出一种个性化解释方法：代理根据用户过往互动，动态构建其世界观模型，作为个人化且持续更新的记忆库，从而估算哪些知识最可能对用户而言是新的。该模型使代理能针对性地提供最相关的解释信息，提升理解效率与用户体验。
原文摘要 · Abstract (English)
The need for explanations in AI has, by and large, been driven by the desire to increase the transparency of black-box machine learning models. However, such explanations, which focus on the internal mechanisms that lead to a specific output, are often unsuitable for non-experts. To facilitate a human-centered perspective on AI explanations, agents need to focus on individuals and their preferences as well as the context in which the explanations are given. This paper proposes a personalized approach to explanation, where the agent tailors the information provided to the user based on what is most likely pertinent to them. We propose a model of the agent's worldview that also serves as a personal and dynamic memory of its previous interactions with the same user, based on which the artificial agent can estimate what part of its knowledge is most likely new information to the user.
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