Forcing Neural Networks to Behave Using Interpretability Methods

Artificial neural networks are excellent machine learning models but are often referred to as “black boxes”, meaning that the reasoning behind their decisions is obscured. The field of neural network interpretability attempts to explain why these models make the decisions they do. In my research I combine methods for interpreting neural network decisions with the neural network training process to develop networks that learn to solve problems in a specified way. Rather than training neural networks only to maximise prediction accuracy, I train the networks while enforcing a constraint that the network’s behaviour interpretation matches our human expectations, with the goal of improving our ability to understand and trust neural networks. Finally, I explore an alternative training objective that seeks to replicate the effects of this guided training method but without the need for a predefined set of human expectations