  People are relying on AI agents to assist them with various tasks. The human
must know when to rely on the agent, collaborate with the agent, or ignore its
suggestions. In this work, we propose to learn rules, grounded in data regions
and described in natural language, that illustrate how the human should
collaborate with the AI. Our novel region discovery algorithm finds local
regions in the data as neighborhoods in an embedding space where prior human
behavior should be corrected. Each region is then described using a large
language model in an iterative and contrastive procedure. We then teach these
rules to the human via an onboarding stage. Through user studies on object
detection and question-answering tasks, we show that our method can lead to
more accurate human-AI teams. We also evaluate our region discovery and
description algorithms separately.
