Algorithmic Explanation: A method for measuring interpretations of neural networks

How do you make good explanations for what a neural network does? We provide a framework for analysing explanations of the behaviour of neural networks by looking at the hypothesis of how they would act on a set of given inputs. By trying to model a neural network using known logic (or as much white-box logic as possible), this framework is a start on how we could tackle neural network interpretability as they get more complex.