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Making Artificial Intelligence (AI)-Mediated Translation Visible: A Minimum Disclosure Standard for Qualitative
1Faculty of Medicine, Nursing and Health Science, Monash University, Clayton, VIC, Australia.
Qualitative Health Research
|June 23, 2026
Summary
Artificial intelligence (AI) translation in qualitative health research risks epistemic injustice by flattening culturally rich data. A Minimum Disclosure Standard for AI-mediated translation (MDS-AIMT) is proposed to ensure AI serves as an assistant, not an author.
Area of Science:
- Qualitative Health Research
- Medical Sociology
- Digital Health
Background:
- Artificial intelligence (AI)-powered translation is increasingly integrated into cross-language qualitative health research.
- Current practices often lack methodological disclosure and theoretical grounding regarding AI translation's impact.
- The normalization of AI translation raises concerns about epistemic issues in knowledge production.
Purpose of the Study:
- To critically examine the epistemic implications of AI-powered translation in qualitative health research.
- To propose a framework for responsible AI translation use in qualitative methodologies.
- To advocate for epistemic accountability in the integration of AI tools.
Main Methods:
- Drawing on scholarship in epistemic injustice and cross-language qualitative research.
- Analyzing the impact of AI translation on culturally nuanced narratives.
- Utilizing Reflexive Thematic Analysis (RTA) as a methodological stress test.
- Proposing a Minimum Disclosure Standard for AI-mediated translation (MDS-AIMT).
Main Results:
- AI translation can flatten culturally saturated qualitative data, leading to epistemic injustice.
- Relying on unverified AI translations challenges the interpretive commitments of methodologies like RTA.
- A proposed MDS-AIMT provides guidelines for AI translation use, disclosure, and verification.
Conclusions:
- AI translation in qualitative health research requires careful methodological consideration and transparent disclosure.
- A robust disclosure standard is essential to mitigate epistemic risks and ensure AI functions as a tool, not an author.
- Promoting epistemic accountability is crucial for the ethical and rigorous use of AI in qualitative health research.
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