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As independently contracted employees, gig workers disproportionately suffer the consequences of workplace surveillance, which include increased pressures to work, breaches of privacy, and decreased digital autonomy. Despite the negative impacts of workplace surveillance, gig workers lack the tools, strategies, and workplace social support to protect themselves against these harms. Meanwhile, some critical theorists have proposed sousveillance as a potential means of countering such abuses of power, whereby those under surveillance monitor those in positions of authority (e.g., gig workers collect data about requesters or platforms). To understand the benefits of sousveillance systems in the gig economy, we conducted semi-structured interviews and led co-design activities with gig workers. We use care ethics as a guiding concept to understand our interview and co-design data, while also focusing on empathic sousveillance technology design recommendations. Through our study, we identify gig workers' attitudes towards and past experiences with sousveillance. We also uncover the types of sousveillance technologies imagined by workers, provide design recommendations, and finish by discussing how to create empowering, empathic spaces on gig platforms.
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This content will become publicly available on January 1, 2026
Gig Work at What Cost? Exploring Privacy Risks of Gig Work Platform Participation in the U.S.
In recent years, gig work platforms have gained popularity as a way for individuals to earn money; as of 2021, 16% of Americans have at some point earned money from such platforms. Despite their popularity and their history of unfair data collection practices and worker safety, little is known about the data collected from workers (and users) by gig platforms and about the privacy dark pattern designs present in their apps. This paper presents an empirical measurement of 16 gig work platforms' data practices in the U.S. We analyze what data is collected by these platforms, and how it is shared and used. Finally, we consider how these practices constitute privacy dark patterns. To that end, we develop a novel combination of methods to address gig-worker-specific challenges in experimentation and data collection, enabling the largest in-depth study of such platforms to date. We find extensive data collection and sharing with 60 third parties—including sharing reversible hashes of worker Social Security Numbers (SSNs)—along with dark patterns that subject workers to greater privacy risk and opportunistically use collected data to nag workers in off-platform messages. We conclude this paper with proposed interdisciplinary mitigations for improving gig worker privacy protections. After we disclosed our SSN-related findings to affected platforms, the platforms confirmed that the issue had been mitigated. This is consistent with our independent audit of the affected platforms. Analysis code and redacted datasets will be made available to those who wish to reproduce our findings.
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- PAR ID:
- 10617874
- Publisher / Repository:
- Proceedings on Privacy Enhancing Technologies Symposium 2025
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2025
- Issue:
- 1
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 491 to 510
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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