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Collective intelligence among gig workers yields considerable ad- vantages, including improved information exchange, deeper social bonds, and stronger advocacy for better labor conditions. Especially as it enables workers to collaboratively pinpoint shared challenges and devise optimal strategies for addressing these issues. However, enabling collective intelligence remains challenging, as existing tools often overestimate gig workers’ available time and uniformity in analytical reasoning. To overcome this, we introduce GigSense, a tool that leverages large language models alongside theories of collective intelligence and sensemaking. GigSense enables gig workers to rapidly understand and address shared challenges effectively, irrespective of their diverse backgrounds. GigSense not only empowers gig workers but also opens new possibilities for supporting workers more broadly, demonstrating the potential of large language model interfaces to enhance collective intelligence efforts in the evolving workplace.
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Together But Alone: Atomization and Peer Support among Gig Workers
The individualistic nature of gig work allows workers to have high levels of flexibility, but it also leads to atomization, leaving them isolated from peer workers. In this paper, we employed a qualitative approach to understand how online social media groups provide informational and emotional support to physical gig workers during the COVID-19 pandemic. We found that social media groups alleviate the atomization effect, as workers use these groups to obtain experiential knowledge from their peers, build connections, and organize collective action. However, we noted a reluctance among workers to share strategic information where there was a perceived risk of being competitively disadvantaged. In addition, we found that the diversity among gig workers has also led to limited empathy for one another, which further impedes the provision of emotional support. While social media groups could potentially become places where workers organize collective efforts, several factors, including the uncertainty of other workers' activities and the understanding of the independent contractor status, have diminished the effectiveness of efforts at collective action.
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- Award ID(s):
- 1952085
- PAR ID:
- 10601527
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW2
- ISSN:
- 2573-0142
- Format(s):
- Medium: X Size: p. 1-29
- Size(s):
- p. 1-29
- Sponsoring Org:
- National Science Foundation
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