Explainable machine learning (XML) has emerged as a major challenge in artificial intelligence (AI). Although black-box models such as Deep Neural Networks and Gradient Boosting often exhibit exceptional predictive accuracy, their lack of interpretability is a notable drawback, particularly in domains requiring transparency and trust. This paper tackles this core AI problem by proposing a novel method to enhance explainability with minimal accuracy loss, using a Mixture of Linear Models (MLM) estimated under the co-supervision of black-box models. We have developed novel methods for estimating MLM by leveraging AI techniques. Specifically, we explore two approaches for partitioning the input space: agglomerative clustering and decision trees. The agglomerative clustering approach provides greater flexibility in model construction, while the decision tree approach further enhances explainability, yielding a decision tree model with linear or logistic regression models at its leaf nodes. Comparative analyses with widely-used and state-of-the-art predictive models demonstrate the effectiveness of our proposed methods. Experimental results show that statistical models can significantly enhance the explainability of AI, thereby broadening their potential for real-world applications. Our findings highlight the critical role that statistical methodologies can play in advancing explainable AI.

翻译：可解释机器学习已成为人工智能领域的一项重大挑战。尽管深度神经网络和梯度提升等黑盒模型通常展现出卓越的预测精度，但其缺乏可解释性是一个显著缺陷，特别是在需要透明度和可信度的应用领域。本文通过提出一种新颖方法来解决这一核心人工智能问题：在保持最小精度损失的前提下，利用黑盒模型协同监督下估计的线性模型混合来增强可解释性。我们开发了基于人工智能技术估计线性模型混合的创新方法。具体而言，我们探索了两种划分输入空间的方法：凝聚聚类和决策树。凝聚聚类方法在模型构建上具有更高的灵活性，而决策树方法则能进一步提升可解释性，生成在叶节点处包含线性或逻辑回归模型的决策树模型。通过与广泛使用及最先进的预测模型进行对比分析，验证了我们所提方法的有效性。实验结果表明，统计模型能显著增强人工智能的可解释性，从而拓宽其在实际应用中的潜力。我们的研究凸显了统计方法在推进可解释人工智能发展中的关键作用。