Machine learning models are incredibly powerful tools. They extract deeply hidden patterns in large data sets that our limited human brains can’t parse. These complex algorithms, then, need to be incomprehensible “black boxes,” because a model that we could crack open and understand would be useless. Right? That’s all wrong, at least according to Cynthia Rudin, who studies interpretable machine...
