Do we still care about how our machine learning does what it does? Today I want to get a bit philosophical and talk about how explainability and risk intersect in machine learning. What do we mean by Explainability? In short, explainability in machine learning is the idea that you could explain to a human user (not necessarily a technically savvy one) how a model is making its decisions. A decision tree is an example of an easily explainable (sometimes called “white box”) model, where you can point to “The model divides the data between houses whose acreage is more than one or less than or equal to one” and so on.