Exploring User Expectations of Proactive AI Systems

Recent advances in artificial intelligence (AI) enabled digital assistants to evolve towards proactive user support. However, expectations as to when and to what extent assistants should take the initiative are still unclear; discrepancies to the actual system behavior might negatively affect user acceptance. In this paper, we present an in-the-wild study for exploring user expectations of such user-supporting AI systems in terms of different proactivity levels and use cases. We collected 3,168 in-situ responses from 272 participants through a mixed method of automated user tracking and context-triggered surveying. Using a data-driven approach, we gain insights into initial expectations and how they depend on different human factors and contexts. Our insights can help to design AI systems with varying degree of proactivity and preset to meet individual expectations.