Interactive AI to Support Human-Human Communication

Abstract

Such important bases of our society as healthcare, education, and productivity typically rely on effective communication between humans. Human-human communication in such settings is often challenging, as it requires advanced communication skills that are not available to everyone. This dissertation argues that systems that leverage models or data about communication can be used to ultimately improve communication. Through two main kinds of studies, this dissertation characterizes challenges when modeling communication from data, as well as when applying these approaches, and it formalizes the problem in such settings. The dissertation introduces systems to model spoken and written communication. It further defines recommendation systems that identify patterns in communication and provides suggestions to people on how to improve their communication. The dissertation also presents designs, implementations and evaluations of systems based on the communication models in the domains of productivity, social media conversations, healthcare, and video broadcasting. The results of experiments evaluating these mechanism show that, compared to current practice, communication models generate new insights, and our AI-human interfaces lead to improved outcomes. The main implication of this dissertation is that design of AI algorithms and user interfaces impact how people communicate with each other. Importantly, technology makes teaching communication skills more accessible, democratizing skills that were only available to experts.