Machine learning models have increasingly demonstrated extraordinary capabilities in prediction tasks across numerous domains. However, as they grow in complexity and accuracy, they often become less interpretable, functioning essentially as “black boxes” where the internal logic remains opaque to human understanding. This tension between predictive power and interpretability represents one of the fundamental challenges in modern artificial intelligence, particularly in high-stakes domains like healthcare, finance, and criminal justice.