Enacting Utopias: A Participatory Design Approach to AI

Overview

This study articulates a participatory design (PD) approach to artificial intelligence that integrates foundational PD emphases on empowerment and local learning with the theoretical construct of enacted utopias. The hybrid framing positions PD between prevailing utopian and dystopian narratives of AI by privileging local agency, collective deliberation, and the capacity to formulate concrete demands for how AI is introduced into work practices. Empirical grounding is provided through a case study in which researchers collaborated with a labor union to operationalize the approach in a series of workshops with 53 union members.

Methods and approach

The approach combines PD principles—co-design, iterative prototyping of practice, and situated learning—with the enacted-utopias concept, which foregrounds cross-sectoral collaboration, expansive learning, and transformative agency. A concrete workshop format was developed to cultivate AI literacy, practical skills, and organizational agency: activities included collective problem articulation, scenario enactment of alternative work practices with AI, role-based negotiation of demands, and reflective synthesis to convert insights into actionable claims. Recruitment and facilitation were conducted in partnership with a union to ensure relevance to workplace contingencies and to enable institutional uptake.

Results

Workshops produced measurable increases in participant-reported knowledge, practical familiarity with AI concepts, and confidence to negotiate workplace implications of AI. Participants generated concrete proposals and demands concerning AI deployment, including specifications for oversight, transparency, and role reconfiguration, which served as artifacts for potential negotiation with employers. The case demonstrated the viability of the workshop format as a replicable PD intervention and substantiated the value of the enacted-utopias frame for orienting PD activities toward both emancipatory learning and actionable bargaining positions.

Implications

The findings indicate that PD, structured around enacted utopias, can operationalize worker empowerment in the context of AI introduction by producing both cognitive capacity and collective instruments for negotiation. Institutional partnerships, particularly with labor organizations, are instrumental for situating PD interventions in existing power structures and for translating workshop outputs into organizational demands. Future work should examine scalability across sectors, longitudinal effects on bargaining outcomes, and mechanisms for integrating PD-generated artifacts into formal governance processes for AI within workplaces.