GigSense: An LLM-Infused Tool for Workers Collective Intelligence

TL;DR

GigSense is a novel LLM-based tool that enhances collective intelligence among gig workers by facilitating rapid understanding and problem-solving of shared challenges, outperforming traditional methods in effectiveness and usability.

Contribution

We introduce GigSense, a new LLM-infused platform that improves collective problem-solving for gig workers, addressing limitations of existing tools and demonstrating enhanced performance and user experience.

Findings

GigSense users identified problems faster than control group.

GigSense produced higher quality solutions in less time.

Users reported better usability with GigSense.

Abstract

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.