Abstract
Interactive Machine Learning (IML) is concerned with the development of algorithms for enabling machines to cooperate with human agents. A challenge is how to integrate people into the learning loop in a way that is transparent, efficient, and beneficial to the human-AI team as a whole, supporting different requirements and users with different levels of expertise. Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. Such advances would enrich the range of applicability of semi-autonomous systems to real-world tasks, most of which involve cooperation with one or more human partners. This workshop aims to bring together researchers from industry and academia and from different disciplines in AI and surrounding areas to explore challenges and innovations in IML.