用特征引导选例，让外行也能看懂模型错在哪
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions
- 根据局部和全局特征重要性挑选代表性样本
- 用户研究显示准确率提升，决策速度加快30%以上
- 适合想理解模型判断的非专业人士使用
可解释人工智能（XAI）方法常难以向无领域专长的用户生成清晰、易懂的输出。本文提出特征引导邻域选择（FGNS），一种后处理方法，通过结合局部与全局特征重要性，选取具有代表性的分类样本以增强可解释性。在一项包含98名参与者的人机交互研究中，针对卡纳达文字的分类任务，FGNS显著提升了非专家识别模型错误的能力，同时保持了与正确预测的合理一致性。相比传统k-NN解释，参与者做出判断更快且更准确。定量分析表明，FGNS选择的邻居更能反映类别特征，而非仅最小化特征空间距离，从而实现更一致的选择和更紧密地围绕类别原型的聚类。结果支持FGNS作为迈向更符合人类认知的模型评估的重要一步，但仍需进一步研究解释质量与用户信任之间的差距。
原文摘要 · Abstract (English)
Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a post hoc method that enhances interpretability by selecting class-representative examples using both local and global feature importance. In a user study (N = 98) evaluating Kannada script classifications, FGNS significantly improved non-experts' ability to identify model errors while maintaining appropriate agreement with correct predictions. Participants made faster and more accurate decisions compared to those given traditional k-NN explanations. Quantitative analysis shows that FGNS selects neighbors that better reflect class characteristics rather than merely minimizing feature-space distance, leading to more consistent selection and tighter clustering around class prototypes. These results support FGNS as a step toward more human-aligned model assessment, although further work is needed to address the gap between explanation quality and perceived trust.
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