Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose REASONX, an explanation method based on Constraint Logic Programming (CLP). REASONX can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of REASONX, which consists of a Python layer, closer to the user, and a CLP layer. REASONX's core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories.

翻译：解释不透明的机器学习(ML)模型是一个日益重要的问题。当前人工智能可解释性(XAI)方法存在若干缺陷，包括背景知识整合不足、缺乏抽象性以及用户交互性不足等。我们提出REASONX——基于约束逻辑编程(CLP)的解释方法。该方法能够为决策树提供声明式、交互式的解释，这些决策树既可以是待分析的机器学习模型，也可以是任意黑盒模型的全局或局部替代模型。用户可通过线性约束及基于事实与对比实例特征的多目标整数线性规划(MILP)优化表达背景知识或常识知识，并借助约束投影在不同抽象层级上与答案约束进行交互。本文呈现REASONX的体系架构，该架构由贴近用户的Python层与CLP层组成。其核心执行引擎是一个元解释器程序，该程序基于逻辑理论具有声明式语义。