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A methodological framework for participatory AI
AI from the Margins
A preparatory stage of participatory AI that centers the lived experiences of minoritized communities, ensuring their perspectives shape AI design from the very beginning.
About
Why it matters
AI is increasingly embedded in healthcare — shaping clinical decision-making, disease detection, and support for daily living. Despite its promise, AI applications can reproduce or amplify existing inequities, creating particular vulnerabilities for minoritized communities.
Participatory AI promises to democratize AI design by involving affected communities. However, much of what is labeled participatory remains instrumental rather than transformative — using participation to optimize systems rather than to genuinely share power, ownership, or agency.
AIM addresses this gap by introducing a preparatory stage of participatory AI design — a stage in which the lived experiences of minoritized communities are explored without technological framing, before problem definitions, success criteria, and design artifacts have been set. This positioning ensures that the voices of those most affected shape what AI is for, not only how it is refined.
Research
The AIM method
AIM operationalizes lived experience as the preparatory stage of participatory AI design. Rather than treating lived experience as an input within existing processes, AIM structures pre-design engagement through four dedicated lived experience sessions, each grounded in commitments to reciprocity, decolonising research, and accountability to participants and the communities they are part of.
Sharing lived experience
Each participant tells their story in their own words, prompted by a single open question (SQUIN — Single Question aimed at Inducing Narrative) drawn from the Biographical Narrative Interpretive Method. Peers ask follow-up questions in a Socratic dialogue, and the session is grounded in reciprocity: participants are partners in producing knowledge, not subjects to be studied.
* May span multiple meetings to ensure every voice is heard fully.Collective rules
Individual narratives become the foundation for collective reflection. Together, participants articulate shared rules for what equitable practice should look like — moving from personal accounts toward principles that participants themselves define and own.
Bridging to participatory AI
Participants extend their lived experience into the participatory AI space, first through visual and narrative methods (including Rich Picture), then by engaging with a concrete AI case. Technical framing is introduced only after lived experience has been articulated on its own terms.
Embedding in AI policy
Policy workers join participants in a structured conversation about how AI policy can safeguard lived experiences. The session is designed to surface — rather than smooth over — the asymmetries between institutional and experiential framings.
Based on the work of
- Costanza-Chock, S. (2020). Design Justice: Community-Led Practices to Build the Worlds We Need.
- Wengraf, T. (2001). Qualitative Research Interviewing: Biographic Narrative and Semi-Structured Methods.
- Collins, P. H. (2000). Black Feminist Thought.
- Smith, L. T. (2012). Decolonizing Methodologies: Research and Indigenous Peoples.
- hooks, b. (1994). Teaching to Transgress: Education as the Practice of Freedom.
- Crenshaw, K. (1991). Mapping the Margins: Intersectionality, Identity Politics, and Violence Against Women of Color.
Publications
Publications
2026
AI From the Margins (AIM): A Method to Create AI Applications Centralizing the Lived Experience of Minoritized Communities
The full paper presenting the AIM method, its theoretical grounding in standpoint theory and decolonising methodologies, and findings from eight lived experience sessions with thirteen minoritized participants in healthcare. Submitted to AIES 2026.
2026
Portegies et al. — Lived experience as a foundation for participatory AI
Earlier work applying the Biographical Narrative Interpretive Method (BNIM) in healthcare AI research, surfacing requirements for AI grounded in participants' own accounts. The methodological foundation that AIM extends into a four-session preparatory framework.
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Get in touch
Interested in the AIM method, a collaboration, or a presentation? Fill in the form below and we will get back to you.
Or email directly at t.c.portegies@uva.nl