# Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?

arXiv: https://arxiv.org/abs/2303.07242 · HTML: https://mlsanity.com/paper/2303.07242/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 115 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- ["What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness](https://mlsanity.com/paper/2011.02282/) (2020)
- [Roles for Computing in Social Change](https://mlsanity.com/paper/1912.04883/) (2019)
- [How Work From Home Affects Collaboration: A Large-Scale Study of Information Workers in a Natural Experiment During COVID-19](https://mlsanity.com/paper/2007.15584/) (2020)
- [AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations](https://mlsanity.com/paper/2101.12284/) (2021)
- [Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbot](https://mlsanity.com/paper/2302.00832/) (2023)
- [Good Intentions, Bad Inventions: How Employees Judge Pervasive Technologies in the Workplace](https://mlsanity.com/paper/2210.06381/) (2022)
- [Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being](https://mlsanity.com/paper/2006.08364/) (2020)
- [(Re)Politicizing Digital Well-Being: Beyond User Engagements](https://mlsanity.com/paper/2203.08199/) (2022)

## Built on by (most cited first, 6 of 6)

- [Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers](https://mlsanity.com/paper/2506.15047/) (2025)
- [Data Feminism for AI](https://mlsanity.com/paper/2405.01286/) (2024)
- [From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interaction](https://mlsanity.com/paper/2601.21920/) (2026)
- [Does AI Coaching Prepare us for Workplace Negotiations?](https://mlsanity.com/paper/2509.22545/) (2025)
- [Sima AIunty: Caste Audit in LLM-Driven Matchmaking](https://mlsanity.com/paper/2603.29288/) (2026)
- [Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions](https://mlsanity.com/paper/2609.16907/) (2026)

## Abstract (arXiv)

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.