提出统一框架，解释模型对特定人群的决策逻辑。
CohEx: A Generalized Framework for Cohort Explanation
- 基于有监督聚类构建群体解释框架
- 支持在特定人群上分析模型行为
- 适合医疗等需群体决策透明的场景
可解释人工智能（XAI）在提升机器学习模型透明度和可信度方面受到广泛关注。然而，现有解释技术多聚焦于全局或单个样本的解释，对群体层面的解释关注较少。群体解释能揭示模型在特定实例群体上的行为模式，深化对模型决策上下文的理解。本文探讨了群体解释的独特挑战与机遇，定义其期望属性，并提出一种基于有监督聚类的通用框架，用于生成群体解释。
原文摘要 · Abstract (English)
eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground, i.e., cohort-based explanation, is less explored. Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances, enabling a deeper understanding of model decisions within a defined context. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations, define their desired properties, and create a generalized framework for generating cohort explanations based on supervised clustering.
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