Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, symbolic expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.

中文翻译：

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应用遗传编程提高机器学习模型的可解释性

可解释人工智能（或 xAI）已成为机器学习和深度学习领域的重要研究课题。在本文中，我们提出了一种基于遗传编程 (GP) 的方法，称为遗传编程解释器 (GPX)，用于解释人工智能系统计算的决策问题。该方法生成位于感兴趣点附近的噪声集，应解释其预测，并为分析的样本拟合局部解释模型。GPX 生成的树结构提供了一种可理解的分析性、可能是非线性的符号表达式，它反映了复杂模型的局部行为。我们考虑了三种可以识别为复杂黑盒模型的机器学习技术：随机森林、用于回归和分类问题的二十个数据集中的深度神经网络和支持向量机。我们的结果表明，GPX 能够比现有技术更准确地理解复杂模型。结果验证了所提出的方法是一种部署 GP 以提高可解释性的新方法。