{"id":945267,"date":"2023-06-01T10:31:26","date_gmt":"2023-06-01T17:31:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=945267"},"modified":"2023-12-21T09:46:16","modified_gmt":"2023-12-21T17:46:16","slug":"can-workers-meaningfully-consent-to-workplace-wellbeing-technologies","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/can-workers-meaningfully-consent-to-workplace-wellbeing-technologies\/","title":{"rendered":"Can Workers Meaningfully Consent To Workplace Wellbeing Technologies?"},"content":{"rendered":"
Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help businesses and workers optimize productivity and wellbeing. However, given the workplace’s inherent and structural power dynamics, the prevalent approach of accepting tacit compliance to monitor work activities rather than seeking workers’ meaningful consent raises privacy and ethical concerns. This paper unpacks the challenges workers face when consenting to workplace wellbeing technologies. Using a hypothetical case to prompt reflection among six multi-stakeholder focus groups involving 15 participants, we explored participants’ expectations and capacity to consent to these technologies. We sketched possible interventions that could better support meaningful consent to workplace wellbeing technologies by drawing on critical computing and feminist scholarship — which reframes consent from a purely individual choice to a structural condition experienced at the individual level that needs to be freely given, reversible, informed, enthusiastic, and specific (FRIES). The focus groups revealed how workers are vulnerable to “meaningless” consent — as they may be subject to power dynamics that minimize their ability to withhold consent and may thus experience an erosion of autonomy, also undermining the value of data gathered in the name of “wellbeing.” To meaningfully consent, participants wanted changes to the technology and to the policies and practices surrounding the technology. Our mapping of what prevents workers from meaningfully consenting to workplace wellbeing technologies (challenges) and what they require to do so (interventions) illustrates how the lack of meaningful consent is a structural problem requiring socio-technical solutions…<\/p>\n","protected":false},"excerpt":{"rendered":"
Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help businesses and workers optimize productivity and wellbeing. However, given the workplace’s inherent and structural power dynamics, the prevalent approach of accepting […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13554,13559],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[246694,253306,267858],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-945267","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-research-area-social-sciences","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-human-computer-interaction-3","msr-field-of-study-social-and-information-networks"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-6-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2303.07242v3","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Shreya Chowdhary","user_id":0,"rest_url":false},{"type":"text","value":"Anna Kawakami","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Mary L. Gray","user_id":32970,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Mary L. 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