A key challenge for the successful deployment of many real world human-facing automated sequential decision-making systems is the need for human-AI collaboration. Effective collaboration ensures that the complementary abilities and skills of the human-users and the AI system are leveraged to maximize utility. This is for instance important in applications such as autonomous driving, in which a human user’s skill might be required in safety critical situations, or virtual personal assistants, in which a human user can perform real-world physical interactions which the AI system cannot. Facilitating such collaboration requires cooperation, coordination, and communication, e.g., in the form of accountability, teaching interactions, provision of feedback, etc. Without effective human-AI collaboration, the utility of automated sequential decision-making systems can be severely limited. Thus there is a surge of interest in better facilitating human-AI collaboration in academia and industry. Most existing research has focussed only on basic approaches for human-AI collaboration with little focus on long-term interactions and the breadth needed for next-generation applications. In this workshop we bring together researchers to advance this important topic, focussing on the following three directions: (a) Accountability and trust; (b) Adaptive behavior for long-term collaboration; (c) Robust collaboration under mismatch.