Co-Designing Collaborative Generative AI Tools for Freelancers

要旨

Most generative AI tools prioritize individual productivity and personalization, with limited support for collaboration. Designed for traditional workplaces, these tools do not fit freelancers' short-term teams or lack of shared institutional support, which can worsen their isolation and overlook freelancing platform dynamics. This mismatch means that, instead of empowering freelancers, current generative AI tools could reinforce existing precarity and make freelancer collaboration harder. To investigate how to design generative AI tools to support freelancer collaboration, we conducted co-design sessions with 27 freelancers. A key concern that emerged was the risk of AI systems compromising their creative agency and work identities when collaborating, especially when AI tools could reproduce content without attribution, threatening the authenticity and distinctiveness of their collaborative work. Freelancers proposed "auxiliary AI" systems, human-guided tools that support their creative agencies and identities, allowing for flexible freelancer-led collaborations that promote "productive friction". Drawing on Marcuse's concept of technological rationality, we argue that freelancers are resisting one-dimensional, efficiency-driven AI, and instead envisioning technologies that preserve their collective creative agencies. We conclude with design recommendations for collaborative generative AI tools for freelancers.

著者
Kashif Imteyaz
Northeastern University, Boston, Massachusetts, United States
Michael Muller
Independent Researcher, Medford, Massachusetts, United States
Claudia Flores-Saviaga
Northeastern University, Northeastern University, Massachusetts, United States
Saiph Savage
Northeastern University, Boston, Massachusetts, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Co-Design

P1 - Room 129
7 件の発表
2026-04-16 20:15:00
2026-04-16 21:45:00