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Passive Sensing–enabled AI (PSAI) uses behavioral traces from workers’ digital tools to infer experience, yet its practical designs can create tensions that drive resistance to adoption. This paper examines PSAI components to maximize value while addressing worker concerns. An interactive online survey with the Experimental Vignette Method and linear mixed-effects models show higher acceptability for sensing digital time use or physical activity, while language-based sensing is acceptable mainly in work-restricted contexts.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/sensible-and-sensitive-ai-for-worker-wellbeing-factors-that-inform-adoption-and-resistance-for-information-workers/511311/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/sensible-and-sensitive-ai-for-worker-wellbeing-factors-that-inform-adoption-and-resistance-for-information-workers/511311.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-11","2026-10-06",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is Passive Sensing–enabled AI (PSAI) in the paper?","Question",{"text":113,"@type":114},"PSAI refers to systems that unobtrusively sense observable phenomena from workers’ environments or tools and use algorithmic modeling to generate experiential inferences such as performance or stress scores.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What sensing modalities were more acceptable to workers?",{"text":118,"@type":114},"Workers showed higher acceptability when PSAI sensed digital time use or physical activity rather than visual modes.",{"name":120,"@type":111,"acceptedAnswer":121},"When were language-based inferences considered acceptable?",{"text":122,"@type":114},"Language-based inferences were acceptable only in work-restricted contexts, according to the study results.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},511311,1791368462,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":41,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers  \nVedant Das Swain∗ [v.dasswain@northeastern.edu](v.dasswain@northeastern.edu)  \nNortheastern University Boston, Massachusetts, USA  \nLan Gao∗ [langao@uchicago.edu](langao@uchicago.edu)[ ](langao@uchicago.edu)University of Chicago Chicago, Illinois, USA  \nAbhirup Mondal  \n[amondal36@gatech.edu](amondal36@gatech.edu)[ ](amondal36@gatech.edu)Georgia Institute of Technology Atlanta, Georgia, USA  \nGregory D. Abowd [g.abowd@northeastern.edu](g.abowd@northeastern.edu)  \nNortheastern University Boston, Massachusetts, USA  \nMunmun De Choudhury  \n[munmund@gatech.edu](munmund@gatech.edu)[ ](munmund@gatech.edu)Georgia Institute of Technology Atlanta, Georgia, USA  \nABSTRACT  \nAlgorithmic 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.  \nCCS CONCEPTS  \n• Human-centered computing → Empirical studies in ubiquitous and mobile computing; • Applied computing → Health informatics; • Security and privacy → Social aspects of security and privacy.  \nKEYWORDS  \ninformation work, future of work, passive sensing, digital phenotyping, technology adoption, impacts, harms, human resource management, worker wellbeing, mental health  \n∗ Performed research while at Georgia Institute of Technology  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nCHI’24, May 11–16, 2024, Honolulu, HI, USA  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-0330-0/24/05  \n[https://doi.org/10.1145/3613904.3642716](https://doi.org/10.1145/3613904.3642716)  \nACM Reference Format:  \nVedant Das Swain, Lan Gao, Abhirup Mondal, Gregory D. Abowd, and Munmun De Choudhury. 2024. Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 30 pages. [https://doi.org/10.1145/3613904.3642716](https://doi.org/10.1145/3613904.3642716)  \n1 INTRODUCTION  \nWe witnessed declining economic activity with COVID-19 and subsequently the tech and media layoffs in 2022 [133] . What was less palpable, was the decline in thriving workers. Information Workers (IW)1 have showed an increasing tendency to detach from work while remaining employed","cbCaikukRlumvJPy","https://ap.wps.com/l/cbCaikukRlumvJPy","pdf",3617292,"English","# Introduction\n## Background on information workers and PSAI\n## Worker concerns and the need for evidence\n# Method\n## Experimental Vignette Method\n## Linear Mixed-effects Models\n# Findings\n## Acceptability by sensing modality and context\n## Preferences for wellbeing insights\n## Resistance to forwarding insights to others","[{\"question\":\"What is Passive Sensing–enabled AI (PSAI) in the paper?\",\"answer\":\"PSAI refers to systems that unobtrusively sense observable phenomena from workers’ environments or tools and use algorithmic modeling to generate experiential inferences such as performance or stress scores.\"},{\"question\":\"What sensing modalities were more acceptable to workers?\",\"answer\":\"Workers showed higher acceptability when PSAI sensed digital time use or physical activity rather than visual modes.\"},{\"question\":\"When were language-based inferences considered acceptable?\",\"answer\":\"Language-based inferences were acceptable only in work-restricted contexts, according to the study results.\"}]","Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers | PDF",1791250514,76]