Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of interactivity. While (dialogue-like) interactivity is important to better communicate an explanation, background knowledge has the potential to significantly improve their quality, e.g., by adapting the explanation to the needs of the end-user. To close this gap, we present REASONX, an explanation tool based on Constraint Logic Programming (CLP). REASONX provides interactive contrastive explanations that can be augmented by background knowledge, and allows to operate under a setting of under-specified information, leading to increased flexibility in the provided explanations. REASONX computes factual and constrative decision rules, as well as closest constrative examples. It provides explanations for decision trees, which can be the ML models under analysis, or global/local surrogate models of any ML model. While the core part of REASONX is built on CLP, we also provide a program layer that allows to compute the explanations via Python, making the tool accessible to a wider audience. We illustrate the capability of REASONX on a synthetic data set, and on a a well-developed example in the credit domain. In both cases, we can show how REASONX can be flexibly used and tailored to the needs of the user.

翻译：许多高性能机器学习模型不具备可解释性。随着这些模型越来越多地应用于可能对个人产生关键影响的决策场景，开发工具以更好理解其输出变得必要。流行的解释方法包括对比解释，但这类方法存在若干缺陷，例如背景知识融入不足以及缺乏交互性。尽管（类似对话的）交互性对于更好地传达解释至关重要，但背景知识有潜力显著提升解释质量（例如通过根据终端用户需求调整解释）。为填补这一空白，我们提出了REASONX——一种基于约束逻辑编程（CLP）的解释工具。REASONX提供可被背景知识增强的交互式对比解释，并允许在信息不充分的设定下运行，从而增强所提供解释的灵活性。REASONX可计算事实决策规则、对比决策规则以及最近对比样本。它为决策树提供解释，这些决策树既可以是待分析的机器学习模型，也可以是任意机器学习模型的全局/局部替代模型。尽管REASONX的核心部分基于CLP构建，我们还提供了一个通过Python计算解释的程序层，使该工具更易于被广泛用户群体使用。我们通过一个合成数据集以及一个信贷领域的典型案例展示了REASONX的能力。在这两个案例中，我们均证明了REASONX可灵活应用并根据用户需求进行定制。