  Explainable Artificial Intelligence (XAI) methods are intended to help human
users better understand the decision making of an AI agent. However, many
modern XAI approaches are unintuitive to end users, particularly those without
prior AI or ML knowledge. In this paper, we present a novel XAI approach we
call Responsibility that identifies the most responsible training example for a
particular decision. This example can then be shown as an explanation: "this is
what I (the AI) learned that led me to do that". We present experimental
results across a number of domains along with the results of an Amazon
Mechanical Turk user study, comparing responsibility and existing XAI methods
on an image classification task. Our results demonstrate that responsibility
can help improve accuracy for both human end users and secondary ML models.
