This paper proposes a new framework for learning a rule ensemble model that
is both accurate and interpretable. A rule ensemble is an interpretable model
based on the linear combination of weighted rules. In practice, we often face
the trade-off between the accuracy and interpretability of rule ensembles. That
is, a rule ensemble needs to include a sufficiently large number of weighted
rules to maintain its accuracy, which harms its interpretability for human
users. To avoid this trade-off and learn an interpretable rule ensemble without
degrading accuracy, we introduce a new concept of interpretability, named local
interpretability, which is evaluated by the total number of rules necessary to
express individual predictions made by the model, rather than to express the
model itself. Then, we propose a regularizer that promotes local
interpretability and develop an efficient algorithm for learning a rule
ensemble with the proposed regularizer by coordinate descent with local search.
Experimental results demonstrated that our method learns rule ensembles that
can explain individual predictions with fewer rules than the existing methods,
including RuleFit, while maintaining comparable accuracy.

本文提出了一种新的框架来学习规则集合模型，该模型既准确又可解释，该模型的可解释性通过评估模型所需表达预测所需的规则数量来评估，并提出了一种促进局部可解释性的正则化器，通过局部搜索的坐标下降算法来学习规则集合。实验结果表明，与现有方法（包括 RuleFit）相比，我们的方法学习规则集合时所需规则数量更少，同时仍然保持相当的准确性。

学习本地可解释规则集合

Learning Locally Interpretable Rule Ensemble

To this day, a variety of approaches for providing local interpretability of
black-box machine learning models have been introduced. Unfortunately, all of
these methods suffer from one or more of the following deficiencies: They are
either difficult to understand themselves, they work on a per-feature basis and
ignore the dependencies between features and/or they only focus on those
features asserting the decision made by the model. To address these points,
this work introduces a reinforcement learning-based approach called Monte Carlo
tree search for eXplainable Artificial Intelligent (McXai) to explain the
decisions of any black-box classification model (classifier). Our method
leverages Monte Carlo tree search and models the process of generating
explanations as two games. In one game, the reward is maximized by finding
feature sets that support the decision of the classifier, while in the second
game, finding feature sets leading to alternative decisions maximizes the
reward. The result is a human friendly representation as a tree structure, in
which each node represents a set of features to be studied with smaller
explanations at the top of the tree. Our experiments show, that the features
found by our method are more informative with respect to classifications than
those found by classical approaches like LIME and SHAP. Furthermore, by also
identifying misleading features, our approach is able to guide towards improved
robustness of the black-box model in many situations.

本文介绍一种基于强化学习技术的可解释人工智能方法，称为 McXai，以解释任何黑盒分类模型的决策。该方法使用 Monte Carlo 树搜索对生成解释的过程进行建模，并利用两个游戏来找到支持分类器决策的特征集合和导致替代决策的特征集合。最终呈现为一棵树形结构，每个节点表示一个要研究的特征集，能够找到比 LIME 和 SHAP 更具信息性的特征，并且能够指出误导特征，从而提高黑盒模型的鲁棒性。

McXai: 两个博弈作为本地模型无关解释

McXai: Local model-agnostic explanation as two games

We propose a fast, model agnostic method for finding interpretable
counterfactual explanations of classifier predictions by using class
prototypes. We show that class prototypes, obtained using either an encoder or
through class specific k-d trees, significantly speed up the the search for
counterfactual instances and result in more interpretable explanations. We
introduce two novel metrics to quantitatively evaluate local interpretability
at the instance level. We use these metrics to illustrate the effectiveness of
our method on an image and tabular dataset, respectively MNIST and Breast
Cancer Wisconsin (Diagnostic). The method also eliminates the computational
bottleneck that arises because of numerical gradient evaluation for
$\textit{black box}$ models.

使用类原型的快速、模型无关方法可以找到分类器预测的可解释因果关系解释，并且该方法通过两个新的度量标准在实例级别上定量评估局部可解释性。该方法在图像数据集 MNIST 和表格数据集 Breast Cancer Wisconsin（Diagnostic）上得到了有效的验证，同时消除了由于数值梯度评估而产生的计算瓶颈。