Making Artificial Intelligence (AI)–Mediated Translation Visible: A Minimum Disclosure Standard for Qualitative Health Research

I offer this manuscript as a methodological provocation for qualitative health research at a moment when artificial intelligence (AI)–powered translation is becoming increasingly normalised in cross-language interviewing, transcription, and analysis. Rather than reiterate the now-familiar claim that “AI translation can be risky,” I argue that the central problem is epistemic: translation technologies are quietly becoming part of the infrastructure of qualitative knowledge production, yet remain methodologically under-disclosed and analytically under-theorised. Drawing on epistemic injustice and cross-language qualitative scholarship, I show how AI translation can flatten culturally saturated narratives. I then explain why Reflexive Thematic Analysis (RTA) functions as a methodological stress test in this terrain: when researchers rely on AI-mediated translations without robust human verification, the interpretive commitments of RTA become difficult to sustain. In response, I propose a guideline: a Minimum Disclosure Standard for AI-mediated translation (MDS-AIMT) in qualitative health research, specifying considerations for authors regarding the selection and use of translation tools, the languages involved, the verification processes employed, data governance protocols, the alignment of analysis methodologies, and the incorporation of AI-specific reflexivity in their work. Finally, I demonstrate a concrete hybrid “repair” strategy—returning to a vignette in which a culturally dense proverb is flattened into “she feels sad about medicine”—to show how AI can assist with access without becoming an unacknowledged co-author of the data. My aim is to move the field toward an ethic of epistemic accountability: technology as an assistant, not an author, and translation as an interpretive practice rather than a methodological footnote.