  Human and AI are increasingly interacting and collaborating to accomplish
various complex tasks in the context of diverse application domains (e.g.,
healthcare, transportation, and creative design). Two dynamic, learning
entities (AI and human) have distinct mental model, expertise, and ability;
such fundamental difference/mismatch offers opportunities for bringing new
perspectives to achieve better results. However, this mismatch can cause
unexpected failure and result in serious consequences. While recent research
has paid much attention to enhancing interpretability or explainability to
allow machine to explain how it makes a decision for supporting humans, this
research argues that there is urging the need for both human and AI should
develop specific, corresponding ability to interact and collaborate with each
other to form a human-AI team to accomplish superior results. This research
introduces a conceptual framework called "Co-Learning," in which people can
learn with/from and grow with AI partners over time. We characterize three key
concepts of co-learning: "mutual understanding," "mutual benefits," and "mutual
growth" for facilitating human-AI collaboration on complex problem solving. We
will present proof-of-concepts to investigate whether and how our approach can
help human-AI team to understand and benefit each other, and ultimately improve
productivity and creativity on creative problem domains. The insights will
contribute to the design of Human-AI collaboration.
