Before revision: We propose a new, complementary approach to interpretability, in which machines are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers.
After revision: The task of optimizing machines to support human decision-making is often conflated with that of optimizing machines for accuracy even though they are materially different. Whereas it is typical for learning systems to prescribe actions through prediction, here we propose an approach in which the role of machines is to reframe problems in order to directly support human decisions. Inspired by the success of representation learning in promoting machine performance, we frame the problem as one of learning representations that are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers.