Towards Social Role-Based Interruptibility Management

TL;DR

This paper explores a social role-based approach to managing interruptions by combining machine learning, on-device sensing, and social science theories to improve attention management systems.

Contribution

It introduces a personalized two-stage classification model that incorporates social roles and life domains for better interruptibility prediction.

Findings

Developed a social role-based attention management framework.

Integrated social science theories with machine learning models.

Discussed privacy and ethical challenges in AI-driven attention systems.

Abstract

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.