Abstract:  In the future, humans will cooperate with a wide range of AI-based systems in both working (i.e., decision and recommender systems, language models, or industry robots) and private (i.e., fully- or semi-automated vehicles, smart home applications, or ubiquitous computing systems) environments. Cooperation with these systems involves shared (i.e., concurrent multitasking) and traded (i.e., task switching) interaction. As it is known that frequently changing attention can yield decreased performance as well as higher error rates and stress, future systems must consider human attention as a limited resource to be perceived as valuable and trustworthy. This talk addresses the emerging problems that occur when users frequently switch their attention between multiple systems or activities and proposes to develop a new class of AI-based interactive systems that integrally manage user attention. Therefore, we designed a software architecture that utilizes reinforcement learning and principles of computational rationality to optimize task switching. While computational rationality allows the system to simulate and adapt to different types of users, reinforcement learning does not require labeled training data so that the concept can be applied to a wide range of tasks. The architecture has demonstrated its potential in laboratory studies and is currently extended to support various multitasking situations. The talk concludes with a critical assessment of the underlying concepts while providing a research agenda to improve cooperation with computer systems.

About the Speaker: Philipp Wintersberger is a Full Professor of Intelligent User Interfaces at IT:U Linz, as well as an external lecturer at TU Wien and FH Hagenberg. He leads an interdisciplinary team of scientists on FWF, FFG, and industry-funded research projects focusing on human-machine cooperation in safety-critical AI-based systems. He has (co)authored various works published at major journals and conferences (such as ACM CHI, IUI, AutomotiveUI, or Human Factors), and his contributions have won several awards. Further, he is a member of the ACM AutomotiveUI steering committee, has contributed to HCI conferences in various roles in the past (Technical Program Chair AutomotiveUI’21, Workshop Chair MuM’23, Diversity and Inclusion Chair Muc’22), and is one of the main organizers of the CHI workshop on Explainable Artificial Intelligence (XAI).