为气象预报模型设计可解释性界面，提升用户决策信心与效率
Explainable AI-Based Interface System for Weather Forecasting Model
- 针对降雨场景、模型推理和输出置信度三类需求，匹配相应可解释方法
- 实验验证解释信息显著提升用户决策效用与信任度，尤其偏好直观展示
- 适合关注人机协同决策的气象从业者及可解释AI系统开发者
机器学习在气象决策中日益普及，但面向用户的可解释人工智能（XAI）研究尚未覆盖此领域。本研究通过用户调研定义了气象黑箱模型解释的三项需求：不同降雨场景下的统计性能以识别模型偏差、模型推理过程、以及输出置信度。对应地映射合适的XAI方法，并对生成的解释进行定量与定性测试。基于用户反馈设计了XAI交互系统。结果表明，解释信息显著提升了决策效用与用户信任。即使在明显易懂的案例中，用户仍更倾向直观解释而非基于算法的说明。这些发现为未来以人为中心的XAI算法研究提供依据，也为实际应用中AI系统的可用性改进奠定基础。
原文摘要 · Abstract (English)
Machine learning (ML) is becoming increasingly popular in meteorological decision-making. Although the literature on explainable artificial intelligence (XAI) is growing steadily, user-centered XAI studies have not extend to this domain yet. This study defines three requirements for explanations of black-box models in meteorology through user studies: statistical model performance for different rainfall scenarios to identify model bias, model reasoning, and the confidence of model outputs. Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively. An XAI interface system is designed based on user feedback. The results indicate that the explanations increase decision utility and user trust. Users prefer intuitive explanations over those based on XAI algorithms even for potentially easy-to-recognize examples. These findings can provide evidence for future research on user-centered XAI algorithms, as well as a basis to improve the usability of AI systems in practice.
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