Characteristic rules have been advocated for their ability to improve
interpretability over discriminative rules within the area of rule learning.
However, the former type of rule has not yet been used by techniques for
explaining predictions. A novel explanation technique, called CEGA
(Characteristic Explanatory General Association rules), is proposed, which
employs association rule mining to aggregate multiple explanations generated by
any standard local explanation technique into a set of characteristic rules. An
empirical investigation is presented, in which CEGA is compared to two
state-of-the-art methods, Anchors and GLocalX, for producing local and
aggregated explanations in the form of discriminative rules. The results
suggest that the proposed approach provides a better trade-off between fidelity
and complexity compared to the two state-of-the-art approaches; CEGA and
Anchors significantly outperform GLocalX with respect to fidelity, while CEGA
and GLocalX significantly outperform Anchors with respect to the number of
generated rules. The effect of changing the format of the explanations of CEGA
to discriminative rules and using LIME and SHAP as local explanation techniques
instead of Anchors are also investigated. The results show that the
characteristic explanatory rules still compete favorably with rules in the
standard discriminative format. The results also indicate that using CEGA in
combination with either SHAP or Anchors consistently leads to a higher fidelity
compared to using LIME as the local explanation technique.

提出了一种名为 CEGA (Characteristic Explanatory General Association rules) 的新颖解释技术，采用关联规则挖掘将多个标准局部解释技术生成的解释聚合为一组特征规则，与两种最先进的方法 Anchors 和 GLocalX 进行比较，结果表明所提出的方法在保真度和复杂度之间提供了更好的权衡，CEGA 和 Anchors 在保真度方面显著优于 GLocalX，而 CEGA 和 GLocalX 在生成的规则数量方面显著优于 Anchors，研究还表明 CEGA 与 SHAP 或 Anchors 结合使用可以实现更高的保真度。

特征规则解释预测

Explaining Predictions by Characteristic Rules

Despite widespread adoption, machine learning models remain mostly black
boxes. Understanding the reasons behind predictions is, however, quite
important in assessing trust, which is fundamental if one plans to take action
based on a prediction, or when choosing whether to deploy a new model. Such
understanding also provides insights into the model, which can be used to
transform an untrustworthy model or prediction into a trustworthy one. In this
work, we propose LIME, a novel explanation technique that explains the
predictions of any classifier in an interpretable and faithful manner, by
learning an interpretable model locally around the prediction. We also propose
a method to explain models by presenting representative individual predictions
and their explanations in a non-redundant way, framing the task as a submodular
optimization problem. We demonstrate the flexibility of these methods by
explaining different models for text (e.g. random forests) and image
classification (e.g. neural networks). We show the utility of explanations via
novel experiments, both simulated and with human subjects, on various scenarios
that require trust: deciding if one should trust a prediction, choosing between
models, improving an untrustworthy classifier, and identifying why a classifier
should not be trusted.

本文提出了一种可解释性的机器学习算法解释技术 ——LIME，基于该技术可以在一个可解释且可靠的小规模模型内对任何分类器的预测进行解释，从而提高了可信度和可理解性，实现了对于信任度的估计及诸多信任场景的适用性。