  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.
