In Interpretable Machine Learning, we add constraints to models to make them easier to understand. I will discuss two benefits of interpretability: improved *troubleshooting* and *scientific discovery*. Troubleshooting is central to computing, which John McCarthy asserted from the 1950's onwards, and it is also central to machine learning. I will discuss how Interpretable Machine Learning enables substantially easier troubleshooting of machine learning models. For tabular data, I will discuss sparse models and the Rashomon Set Paradigm. In this paradigm, the goal is to find all low loss models from a given function class and visualize them to enable user interaction. This paradigm changes machine learning to include not just optimization but enumeration and visualization. It reshapes the way we think about developing models, resolving the "interaction bottleneck" that makes it difficult to interact with classical machine learning algorithms and troubleshoot them. Interpretable Machine Learning also enables scientific discovery. I will discuss a discovery we made concerning computer-aided mammography with interpretable neural networks. My team found subtle asymmetries that predict breast cancer up to 5 years in advance.