Title: Passive Sensing Frameworks for the Future of Information Workers
Date: Wednesday, July 19th, 2023
Time: 3:00 PM – 6:00 PM Eastern Time
Location (in-person): Coda 1215 Coda c1115
Location (remote): Zoom - https://gatech.zoom.us/j/98258539980?pwd=RS9LaGZTYUtCSmcvcWhJaFN6dFdsZz09
Vedant Das Swain
Ph.D. Candidate, Computer Science
School of Interactive Computing
Georgia Institute of Technology
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, Human-Computer Interaction Institute, Carnegie Mellon University (formerly School of IC, Georgia Tech at time of proposal)
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 toward 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. This dissertation demonstrates the potential of repurposing everyday digital technology as an ecological lens to explain their performance and wellbeing. I have studied various technology readily available in information work, such as wearables, mobiles, desktops, Bluetooth beacons, WiFi router networks, and social media. My research presents (i) the utility of passively explaining worker wellbeing with behavioral traces and (ii) the acceptability of deploying such technologies for information work. In my studies, I applied statistical modeling and machine learning to show new ways to clarify indicators of worker experiences at the individual, group, and organizational levels. Later, I took a worker-centric perspective to situate such algorithmic inferences in today's work paradigm and describe the methodological and socio-technical challenges.
My dissertation contributes to the future of work along multiple dimensions. First, it adds to behavioral computing research by showing computationally efficient and versatile opportunities to model passively collected behavioral traces and provides insight into worker effectiveness. Next, it refines organizational science by providing new opportunities to explain worker experiences by accounting for previously unforeseen behavioral dynamics. Last, it highlights the limits of this approach and provides evidence to suggest how these technologies should (and should not) manifest in the workplace. Collectively, my research aims to help workers by underscoring passive sensing practices that are more holistic, accurate, and humane.
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