用群体反事实解释追踪模型决策变化，揭示概念漂移根源
Explaining Concept Drift through the Evolution of Group Counterfactuals
- 通过分析群体反事实解释的演化轨迹，追踪决策逻辑变化
- 能区分数据空间偏移与概念重定义等不同漂移成因
- 适合需要可解释性诊断的动态系统开发者
动态环境中的机器学习模型常受概念漂移影响，数据分布变化导致性能下降。尽管漂移检测已较为成熟，但解释模型决策逻辑如何演变仍具挑战。本文提出一种新方法，通过分析基于群体的反事实解释（Group Counterfactual Explanations, GCEs）随时间的演变来解释概念漂移。该方法追踪漂移前后GCE聚类中心及其关联的反事实动作向量的变化，这些演化特征作为可解释代理，揭示模型决策边界及内在推理机制的结构性变化。我们构建了一个三层框架，融合数据层（分布偏移）、模型层（预测分歧）和提出的解释层，实现对漂移的全面诊断，可有效区分空间数据偏移与概念重标签等不同根本原因。
原文摘要 · Abstract (English)
Machine learning models in dynamic environments often suffer from concept drift, where changes in the data distribution degrade performance. While detecting this drift is a well-studied topic, explaining how and why the model's decision-making logic changes still remains a significant challenge. In this paper, we introduce a novel methodology to explain concept drift by analyzing the temporal evolution of group-based counterfactual explanations (GCEs). Our approach tracks shifts in the GCEs' cluster centroids and their associated counterfactual action vectors before and after a drift. These evolving GCEs act as an interpretable proxy, revealing structural changes in the model's decision boundary and its underlying rationale. We operationalize this analysis within a three-layer framework that synergistically combines insights from the data layer (distributional shifts), the model layer (prediction disagreement), and our proposed explanation layer. We show that such holistic view allows for a more comprehensive diagnosis of drift, making it possible to distinguish between different root causes, such as a spatial data shift versus a re-labeling of concepts.
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