The ever-increasing number of devices quantifying our lives offers a perspective of high awareness of one's wellbeing, yet it remains a challenge for personal informatics (PI) to effectively support data-based reflection. Effective reflection is recognised as a key factor for PI technologies to foster wellbeing. Here, we investigate whether building tangible representations of health data can offer engaging and reflective experiences. We conducted a between-subjects study where n=60 participants explored their immediate blood pressure data in relation to medical norms. They either used a standard mobile app, built a data representation from LEGO bricks based on instructions, or completed a free-form brick build. We found that building with instructions fostered more comparison and using bricks fostered focused attention. The free-form condition required extra time to complete, and lacked usability. Our work shows that designing instructional physicalisation experiences for PI is a means of improving engagement and understanding of personal data.
CatAlyst uses generative models to help workers’ progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers to resume their tasks by generating a continuation of their work and presenting it as an intervention that is more context-aware than conventional (predetermined) feedback. The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy. This frees CatAlyst from domain-specific model-tuning and makes it applicable to various tasks. Our studies involving writing and slide-editing tasks demonstrated CatAlyst’s effectiveness in helping workers swiftly resume tasks with a lowered cognitive load. The results suggest a new form of human-AI collaboration where large generative models publicly available but imperfect for each individual domain can contribute to workers’ digital well-being.
This paper examines the social, technological, and emotional labor of maintaining China’s data-driven governance broadly, and dynamic zero-COVID management in particular. Drawing on ethnographic research in China, we examine the sociotechnical work of maintenance during the 2022 Shanghai lockdown. This labor included coordinating mass testing, quarantine, and lockdown procedures as well as implementing ad-hoc technological workarounds and managing public sentiments. We demonstrate that, far from being effected from the top down, China’s data-driven governance relies on the circumscribed participation of citizens. During Shanghai’s lockdown, citizens with relevant expertise helped to maintain technological stability by fixing or programming data systems, but also to ensure the ongoing production of “positive feelings” about social stability through data-driven governance. In so doing, such citizens simultaneously enacted an ambivalent and limited form of agency, and maintained social and by extension political stability. This article sheds light on data-driven governance and political processes of maintenance.
We examine how two prominent food delivery platforms in India, Swiggy and Zomato, produce a managed digital workforce using a combination of algorithmic control and traditional labor management strategies. Our findings draw from interviews conducted with 13 food delivery workers and a critical discourse analysis of news media coverage. We found that the two platforms combine piece wage restructuring, granular datafication practices, and the use of benevolent language as neoliberal social control mechanisms. We find that this combination of technological governance and strategic managerial practices is a mutually constitutive method of control that restructures labor processes, extracts workers’ compliance and consent, and prevents work disruption. We show that contemporary platform companies draw from strategies that have historically been deployed in industrial labor management. By examining how older and newer regimes of social control and exploitation are strategically intertwined in contemporary platform design, we contribute a historically situated understanding of platform labor that moves beyond dualistic interpretations of “traditional” labor management practices and more recent algorithmic modes of control. Our findings contribute to recent debates in tech labor and algorithmic control by examining how contemporary conditions of precarious work reactivate certain past forms of control and in doing so normalize extreme overwork, exhaustion, speedups, and injuries.
Mutual awareness of visual attention is crucial for successful collaboration. Previous research has explored various ways to represent visual attention, such as field-of-view visualizations and cursor visualizations based on eye-tracking, but these methods have limitations. Verbal communication is often utilized as a complementary strategy to overcome such disadvantages. This paper proposes a novel method that combines verbal communication with the Cone of Vision to improve gaze inference and mutual awareness in VR. We conducted a within-group study with pairs of participants who performed a collaborative analysis of data visualizations in VR. We found that our proposed method provides a better approximation of eye gaze than the approximation provided by head direction. Furthermore, we release the first collaborative head, eyes, and verbal behaviour dataset. The results of this study provide a foundation for investigating the potential of verbal communication as a tool for enhancing visual cues for joint attention.