research-article

Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers

Authors:

Vedant Das Swain,

Abhirup Mondal,

Gregory D. Abowd,

Munmun De ChoudhuryAuthors Info & Claims

CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

Article No.: 104, Pages 1 - 30

https://doi.org/10.1145/3613904.3642716

Published: 11 May 2024 Publication History

Abstract

Algorithmic estimations of worker behavior are gaining popularity. Passive Sensing–enabled AI (PSAI) systems leverage behavioral traces from workers’ digital tools to infer their experience. Despite their conceptual promise, the practical designs of these systems elicit tensions that lead to workers resisting adoption. This paper teases apart the monolithic representation of PSAI by investigating system components that maximize value and mitigate concerns. We conducted an interactive online survey using the Experimental Vignette Method. Using Linear Mixed-effects Models we found that PSAI systems were more acceptable when sensing digital time use or physical activity, instead of visual modes. Inferences using language were only acceptable in work-restricted contexts. Compared to insights into performance, workers preferred insights into mental wellbeing. However, they resisted systems that automatically forwarded these insights to others. Our findings provide a template to reflect on existing systems and plan future implementations of PSAI to be more worker-centered.

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Cited By

Bedmutha MKarumudi SPatrick KRataj HWeibel NKostakos VKay JHoang T(2024)Exploring User Willingness towards Mobile Sensing and Intervention: A Case Study on Mental Health of Undergraduate College StudentsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678422(721-728)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678422
Das Swain VSaha K(2024)Teacher, Trainer, Counsel, Spy: How Generative AI can Bridge or Widen the Gaps in Worker-Centric Digital Phenotyping of WellbeingProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3663384.3663401(1-13)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3663384.3663401

Index Terms

Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

Index terms have been assigned to the content through auto-classification.

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CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

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DOI:10.1145/3613904

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Monash University/New Mexico State University
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Bedmutha MKarumudi SPatrick KRataj HWeibel NKostakos VKay JHoang T(2024)Exploring User Willingness towards Mobile Sensing and Intervention: A Case Study on Mental Health of Undergraduate College StudentsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678422(721-728)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678422
Das Swain VSaha K(2024)Teacher, Trainer, Counsel, Spy: How Generative AI can Bridge or Widen the Gaps in Worker-Centric Digital Phenotyping of WellbeingProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3663384.3663401(1-13)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3663384.3663401

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