通过案例对比，让气象预测模型的推理过程更透明可懂。
Example-Based Concept Analysis Framework for Deep Weather Forecast Models
- 基于用户需求构建案例分析框架，找出与目标案例相似的推理模式。
- 提供可视化案例和概念置信度，帮助识别气象机制中的模糊点。
- 结合人工标注数据与交互界面，提升气象专家的理解效率。
为提升AI模型的可信度，理解其推理过程至关重要，尤其在天气预报这类高风险领域，识别潜在气象机制与预测精度同等重要。尽管已有大量可解释AI研究，但多数方法因过于依赖技术视角而难以应用。为此，我们提出一种以用户为中心的案例化概念分析框架，能够从目标模型中识别出与特定实例具有相似推理路径的案例，并以人类可理解的方式呈现。该框架提供视觉和概念上相近的案例，以及概念归属的概率，以解决气象机制中的歧义问题。为弥合模型向量表示与人类理解之间的差距，我们构建了人工标注的概念数据集，并开发了辅助领域专家参与的用户界面。
原文摘要 · Abstract (English)
To improve the trustworthiness of an AI model, finding consistent, understandable representations of its inference process is essential. This understanding is particularly important in high-stakes operations such as weather forecasting, where the identification of underlying meteorological mechanisms is as critical as the accuracy of the predictions. Despite the growing literature that addresses this issue through explainable AI, the applicability of their solutions is often limited due to their AI-centric development. To fill this gap, we follow a user-centric process to develop an example-based concept analysis framework, which identifies cases that follow a similar inference process as the target instance in a target model and presents them in a user-comprehensible format. Our framework provides the users with visually and conceptually analogous examples, including the probability of concept assignment to resolve ambiguities in weather mechanisms. To bridge the gap between vector representations identified from models and human-understandable explanations, we compile a human-annotated concept dataset and implement a user interface to assist domain experts involved in the the framework development.
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