Toward Social Role-Based Interruptibility Management

Pervasive and ubiquitous computing facilitates immediate access to information in the sense of always-on. Information, such as news, messages, or reminders, can significantly enhance our daily routines but are rendered useless or disturbing when not being aligned with our intrinsic interruptibility preferences. Attention management systems use machine learning to identify short-term opportune moments, so that information delivery leads to fewer interruptions. Humans' intrinsic interruptibility preferences-established for and across social roles and life domains-would complement short-term attention and interruption management approaches. In this article, we present our comprehensive results toward social role-based attention and interruptibility management. Our approach combines on-device sensing and machine learning with theories from social science to form a personalized two-stage classification model. Finally, we discuss the challenges of the current and future AI-driven attention management systems concerning privacy, ethical issues, and future directions.

Automated summary

Pervasive and ubiquitous computing provides immediate access to information, which can enhance daily routines but may be disruptive if not aligned with users' interruptibility preferences. Attention management systems use machine learning to identify opportune moments for information delivery and reduce interruptions. Our comprehensive approach combines on-device sensing, machine learning, and theories from social science to create a personalized two-stage classification model based on social role-based attention and interruptibility management.