With the popularity of deep neural networks (DNNs), model interpretability is
becoming a critical concern. Many approaches have been developed to tackle the
problem through post-hoc analysis, such as explaining how predictions are made
or understanding the meaning of neurons in middle layers. Nevertheless, these
methods can only discover the patterns or rules that naturally exist in models.
In this work, rather than relying on post-hoc schemes, we proactively instill
knowledge to alter the representation of human-understandable concepts in
hidden layers. Specifically, we use a hierarchical tree of semantic concepts to
store the knowledge, which is leveraged to regularize the representations of
image data instances while training deep models. The axes of the latent space
are aligned with the semantic concepts, where the hierarchical relations
between concepts are also preserved. Experiments on real-world image datasets
show that our method improves model interpretability, showing better
disentanglement of semantic concepts, without negatively affecting model
classification performance.

通过在深度模型中主动注入知识，使用语义概念的层次树规范化图像数据实例的表达方式，提高模型可解释性，改善语义概念的分离，并不会对模型的分类性能产生负面影响。

层次语义树概念白化用于可解释的图像分类

Hierarchical Semantic Tree Concept Whitening for Interpretable Image  Classification

Counterfactual explanations are one of the most popular methods to make
predictions of black box machine learning models interpretable by providing
explanations in the form of `what-if scenarios'. Most current approaches
optimize a collapsed, weighted sum of multiple objectives, which are naturally
difficult to balance a-priori. We propose the Multi-Objective Counterfactuals
(MOC) method, which translates the counterfactual search into a multi-objective
optimization problem. Our approach not only returns a diverse set of
counterfactuals with different trade-offs between the proposed objectives, but
also maintains diversity in feature space. This enables a more detailed
post-hoc analysis to facilitate better understanding and also more options for
actionable user responses to change the predicted outcome. Our approach is also
model-agnostic and works for numerical and categorical input features. We show
the usefulness of MOC in concrete cases and compare our approach with
state-of-the-art methods for counterfactual explanations.

该研究提出了一种多目标反事实解释方法（MOC），将反事实搜索转化为多目标优化问题，通过对特征空间的多样性维护，返回一组不同权衡的反事实，并在实际案例中展示了 MOC 的有用性和与现有方法的对比。