AI From the Margins (AIM): Rethinking Participatory AI Design Through the Lived Experience of Minoritized Communities

2026-05-31

AI Summary

This study addresses a critical limitation in participatory AI design, wherein marginalized groups are typically engaged only after problem definition, thereby constraining their ability to shape system objectives or address structural inequities. To counter this, the authors propose an “AI from the Margins” (AIM) methodology that positions the lived experiences of marginalized communities as a foundational prerequisite—not a late-stage input—in AI design. Through the Biographic-Narrative Interpretive Method (BNIM), participants generate narratives, co-develop governance rules, and retain decision-making authority over whether, where, and how AI intervenes. Their insights are further translated into policy through structured dialogues with policymakers. Implemented across eight workshops in Dutch healthcare settings, the approach was validated by participants who affirmed its substantive relevance and advocated for its continuity, demonstrating that early, experience-centered participation can fundamentally reorient the goals and trajectories of participatory AI.

Abstract

Artificial intelligence (AI) can reproduce and amplify the structural inequities faced by minoritized communities. Participatory AI has been proposed as a response, but participation typically starts after problem definitions and success criteria have been set, leaving limited room for minoritized communities to reshape what an AI system is for. We propose AI From the Margins (AIM): a methodological stance that articulates the conditions under which lived experiences of minoritized communities can be elicited, centered, and carried forward to inform participatory AI design. AIM is not a fixed protocol; it articulates a set of preconditions that can be enacted through different techniques in different settings. We applied AIM in a Dutch healthcare context in eight sessions with 13 women and non-binary people of color and five municipal policy workers, namely through (1) narrative elicitation using the Biographic Narrative Interpretive Method (BNIM); (2) co-constructed rule-making; (3) participants' determination of whether, where, and how AI should be involved; and (4) translating lived experience into AI policy through dialogue with policymakers. In their reflections on the sessions, participants described the engagement as substantive and called for its continuation, demonstrating how preparatory orientation fundamentally grounded in lived experience shapes what participatory AI design is for.

Problem

• participatory AI
• minoritized communities
• structural inequities
• lived experience
• AI design

Innovation

• Participatory AI
• Lived Experience
• Marginalized Communities
• AI Ethics
• Co-design