As machine learning models become more complex, understanding how they reach their conclusions becomes increasingly challenging. This article explores the field of explainable AI.

We'll examine techniques for making black-box models more transparent and the importance of interpretability in high-stakes applications like healthcare and finance.

The balance between performance and explainability represents one of the key challenges in modern machine learning.