Title: Passive Sensing Frameworks for the Future of Information Workers

Date: Tuesday, January 25, 2022

Time: 3:30 PM - 6:30 PM ET

Location (in-person):  CODA C1315

Location (remote):  click here to join via Zoom 

Vedant Das Swain

PhD Student, Computer Science

School of Interactive Computing, Georgia Tech

Committee:

Dr. Munmun De Choudhury (co-advisor), School of Interactive Computing, Georgia Institute of Technology 

Dr. Gregory D. Abowd (co-advisor), School of Interactive Computing, Georgia Institute of Technology & College of Engineering, Northeastern University

Dr. Sauvik Das, School of Interactive Computing, Georgia Institute of Technology 

Dr. Thomas Plötz, School of Interactive Computing, Georgia Institute of Technology 

Dr. Anind Dey, Information School, University of Washington

Dr. Shamsi T. Iqbal, Viva Insights, Microsoft Research Redmond

Abstract:

Work sustains our livelihoods and is key to leading a fulfilling life. Improving our effectiveness at work helps us progress towards our goals and reclaim our lives for other activities. Traditionally we have used surveys to understand what makes workers more effective. However, these approaches do not sufficiently reflect workers as a part of a complex ecology --- comprising their daily activities, social dynamics, and the larger community. My thesis posits an alternative and more holistic approach. We can gain a more naturalistic understanding of worker effectiveness by leveraging everyday digital technology dispersed in their ecology as passive sensors.

I focus my studies on information workers, a significant portion of white-collar work. My completed work demonstrates the efficacy of repurposing everyday digital technology as an ecological lens to explain their performance and wellbeing. For this purpose, I have studied various technology readily available in information work. This includes wearables, mobiles, desktops, Bluetooth beacons, WiFi router networks, and social media. Across my studies, I have applied statistical modeling and machine learning to show new ways to clarify indicators of worker experiences.

Yet, these dynamic data-driven insights do not imply that workers would readily adopt these frameworks in actual deployments. Without consideration of the workers who are being sensed, the insights from these frameworks can be exploitative. My proposed research aims to design these frameworks as data flows that respect information workers' privacy awareness in the asymmetrical power dynamics of work. I intend to characterize implicit factors in the design of passive sensing frameworks that determine its perceived utility and risks. In this study, I will follow the experimental vignette method to highlight the tradeoffs for different design decisions by investigating preferences for a variety of passive sensing scenarios inspired by my completed work. Overall, my thesis is motivated to guide subsequent research to passively understand work and workers by underscoring practices to make passive sensing more holistic, accurate, and humane.