This work introduces the notion of intermediate concepts based on levels structure to aid explainability for black-box models. The levels structure is a hierarchical structure in which each level corresponds to features of a dataset (i.e., a player-set partition). The level of coarseness increases from the trivial set, which only comprises singletons, to the set, which only contains the grand coalition. In addition, it is possible to establish meronomies, i.e., part-whole relationships, via a domain expert that can be utilised to generate explanations at an abstract level. We illustrate the usability of this approach in a real-world car model example and the Titanic dataset, where intermediate concepts aid in explainability at different levels of abstraction.

翻译：本文引入了基于层级结构的中间概念，以增强黑盒模型的可解释性。层级结构是一种层次化框架，其中每一层级对应数据集的特征（即玩家集划分）。粗粒度层级从仅包含单元素成员的平凡集合逐渐过渡到仅包含全局联盟的集合。此外，通过领域专家可建立部分-整体关系（即分层关系），这些关系可用于生成抽象层级的解释。我们通过真实世界的汽车模型案例和泰坦尼克数据集验证了该方法的实用性——中间概念在不同抽象层级上均有助于提升模型解释性。