Survey on Explainable AI for Traditional Machine Learning and Domains

This is an academic survey article that reviews methods for making traditional machine learning models more explainable and interpretable across different fields. The survey covers techniques that help users understand how machine learning models make decisions, rather than treating them as "black boxes" where the reasoning is hidden. It was published in a peer-reviewed computer science journal in September 2026.