Gig2Gether: Datasharing to Empower, Unify and Demistify Gig Work

要旨

The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future uses cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact.

受賞
Honorable Mention
著者
Jane Hsieh
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Angie Zhang
University of Texas at Austin, Austin, Texas, United States
Sajel Surati
Bowdoin College, Brunswick, Maine, United States
Sijia Xie
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Yeshua Ayala
Washington University in St. Louis, St. Louis, Missouri, United States
Nithila Sathiya
University of Texas at Austin, Austin, Texas, United States
Tzu-Sheng Kuo
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Min Kyung Lee
University of Texas at Austin, Austin, Texas, United States
Haiyi Zhu
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
DOI

10.1145/3706598.3714398

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714398

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Better Work and Career

G414+G415
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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