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. The objective of these models is to communicate to a human decision-maker not what to decide but how to decide. In this way, we propose that machine learning pipelines will be more readily adopted, since they allow a decision-maker to retain agency. Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs ( " advice") that are effective for human decision-making.
After revision: We propose a new, complementary approach to interpretability, in which machines are conducive to good human performance. This " advice") that are effective for human decision-making.