The importance of interpretability is relative. For Netflix, if a prediction goes awry and a poor recommendation is made, aside from a monetary loss, the consequence is minimal. In this case, the "risk" incurred by deploying predictive models is easily outweighed by the benefit. But what about when machine learning is used to diagnose patients or determine credit worthiness? In these cases, explainability is not only important, it may be a regulatory concern -- especially in heavily regulated industries such as banking, medicine, and insurance.