The Building Blocks of Interpretability - Distill

With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building confidence about how they will behave in the real-world, detecting model bias, and for scientific curiosity. Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them —  and the rich structure of this combinatorial space.