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This paper studies the design of an AI tool that supports gig knowledge workers, rather than displacing them, focusing on text-based generative AI technologies. Through a formative study involving interviews and design activities, gig workers shared their views on text-based generative AI and envisioned applications where AI acts as managers, secretaries, and communication aids. Leveraging these insights, we created a generative-AI enhanced tool, Office-Mind AI, to aid gig workers. Our research advances the conversation around algorithmic labor by designing a worker-focused intelligent tool. This tool harness collective intelligence among workers and AI, fostering productive human-AI partnerships. We conclude by discussing the future prospects of collective intelligence tools designed for worker-AI collaborations.
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This content will become publicly available on November 30, 2025
GigSense: An LLM-Infused Tool for Workers’ Collective Intelligence
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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- Award ID(s):
- 2339443
- PAR ID:
- 10590832
- Publisher / Repository:
- Avances en Interacción Humano-Computadora
- Date Published:
- Journal Name:
- Avances en Interacción Humano-Computadora
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2594-2352
- Page Range / eLocation ID:
- 135 to 145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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