Towards Social Role-Based Interruptibility Management
This work addresses the issue of disruptive notifications in pervasive computing for users, though it appears incremental as it builds on existing short-term attention management approaches.
The paper tackles the problem of aligning information delivery with human interruptibility preferences by proposing a social role-based attention management system, resulting in a personalized two-stage classification model that integrates on-device sensing and social science theories.
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 towards 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.