Co-Designing Collaborative Generative AI Tools for Freelancers

2026-02-05

AI Summary

This study addresses how prevailing generative AI tools prioritize individual efficiency at the expense of freelancers’ distinctive needs in short-term collaboration, identity formation, and platform dynamics, potentially exacerbating their isolation and precarity. Drawing on Marcuse’s critique of technological rationality, the research employs a participatory co-design approach with 27 freelancers to explore alternative design pathways for collaborative generative AI. It proposes the concept of “supportive AI,” which challenges the one-dimensional logic of efficiency and instead advocates for a human-led collaborative paradigm that safeguards creative autonomy and embraces “productive friction.” From this, three core design principles—belonging, attributability, and human guidance—are articulated to inform the development of AI tools that foster collective creative agency and reinforce professional identity among freelancers.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Artificial General Intelligence
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• generative AI
• freelancers
• collaboration
• creative agency
• technological rationality

Innovation

• co-design
• generative AI
• freelancer collaboration
• creative agency
• auxiliary AI