Coachable AI

AI systems that are compatible with human cognition will not come as an afterthought by patching up opaque models with post-hoc explainability. This article reports on our research program to develop Machine Coaching, a computationally efficient and cognitively light paradigm that supports a form of interactive and developmental machine learning, through supervision that is not only functional, on "what" decisions should be made, but also structural, on "how" they should be made. We discuss the role that this paradigm can play in neural-symbolic systems, in operationalizing fast intuitive and slow deliberative thinking, and in supporting the explainability and contestability of AI systems by design.