AI interpretability focuses on making machine learning models comprehensible to humans. As models grow more intricate—particularly with the rise of deep learning architectures—it becomes increasingly difficult to trace how these systems make decisions. Deep neural networks, for example, involve millions of parameters distributed across numerous layers, creating what