AI ethics frameworks often treat fairness, transparency, and accountability as universal values that can be uniformly operationalized across any context.
The study interviewed fourteen experts from ten countries to examine how they understand and practice AI in real projects. Findings reveal that AI deployment typically occurs under structurally unequal conditions, constrained by infrastructure limits, extractive practices, and a mystification of technology, which shape perceptions of risk and opportunity.
Experts reinterpret core values according to local moral logics: privacy becomes collective and relational rather than individual; transparency is seen as trust‑building accountability instead of mere technical disclosure; fairness is understood as equity in access and representation rather than strict outcome parity.
These divergences constitute translation gaps between global frameworks and situated local practices.
To bridge the gaps, the authors propose pluralistic governance pathways: redistributing epistemic authority so that ethical negotiation remains an ongoing, context‑sensitive process rather than a settled technical standard.
Blogger's Review: The paper underscores that ethical principles cannot be rigidly imposed without regard to concrete social structures; only through local negotiation and adaptation can truly responsible AI be achieved.