  Collective intelligence among gig workers yields considerable advantages,
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. Our user study showed that GigSense users
outperformed those using a control interface in problem identification and
generated solutions more quickly and of higher quality, with better usability
experiences reported. GigSense not only empowers gig workers but also opens up
new possibilities for supporting workers more broadly, demonstrating the
potential of large language model interfaces to enhance collective intelligence
efforts in the evolving workplace.
