Interpretable Deep Learning

Interpretable models expose the internal reasoning of a system in a form that is understandable to humans. Once limited by accuracy, they now achieve performance comparable to powerful opaque models such as deep neural networks, and are increasingly adopted to diagnose failures, assess fairness, and satisfy legal and regulatory requirements.