Explaining Explanation, Part 4: A Deep Dive on Deep Nets
Robert R. Hoffman, Tim Miller, Shane T. Mueller, Gary A. Klein, William J. Clancey · IEEE Intelligent Systems · 2018
This is the fourth in a series of essays about "explainable AI." Previous essays laid out the theoretical and empirical foundations. This essay focuses on Deep Nets, and considers methods for allowing system users to generate self-explanations. This is accomplished by exploring how the Deep Net systems perform when they are operating at their "boundary conditions." Inspired by recent research into adversarial examples that demonstrate the weaknesses of Deep Nets, we invert the purpose of these adversarial examples and argue that spoofing can be used as a tool to answer contrastive explanation questions via user-driven exploration.