Generative Autoethnography and the AI-Human Interface: Toward Decolonial Modes of Knowing in Psychiatry and Beyond

Abstract (English)

This paper introduces generative autoethnography, a methodological innovation that emerges at the intersection of anthropology, cognitive science, and lived psychiatric experience. Drawing from my doctoral research on bipolar disorder (BD), the project transitions from individual narrative, rooted in autoethnographic reflection on embodied time and space, into a collective and dialogical mode of inquiry enabled by AI-human collaboration.
By working with conversational AI (e.g., GPT-based models) as epistemic partners, I experiment with a transdisciplinary framework that blends neurophenomenology, sociological theory, and performative writing. This approach questions the authority of expert psychiatric discourse and introduces situated, reflexive, and affectively engaged ways of knowing mental illness. Generative autoethnography thus becomes a tool for anthropological engagement with epistemic decolonisation, offering an alternative to reductive and pathologizing accounts by foregrounding relationality, temporality, and socio-material entanglements.
In dialogue with the practices of European social clinics—such as Village 2 Santé (France), Solidarisches Gesundheitszentrum (Germany), and the Social Solidarity Clinic in Thessaloniki (Greece), all members of the International Network of Social Clinics (INOSC)—the research highlights collective and grassroots forms of mental health care. These clinics model non-extractive, non-hierarchical approaches that challenge biomedical capitalism and cultivate what I term communities of epistemic care.
By mobilizing generative AI, embodied knowledge, and community practices, I explore how anthropological research can contribute to pluralistic, post-disciplinary, and politically engaged understandings of mental suffering. The project aims to open new ethical and methodological horizons for anthropological psychiatry, somatic anthropology, and qualitative research.