  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.
