Lost in Translation: How Universal Ethical Values Fail to Translate Across Global Contexts

Universal AI ethics frameworks don't work the same way everywhere: The paper found that core values like fairness, transparency, and accountability are interpreted differently across the 10 countries studied, meaning a one-size-fits-all approach to responsible AI misses crucial local context.

Power imbalances shape how people experience AI risks: Structural inequalities—including limited infrastructure, extractive business practices, and deliberate obscuring of how technology works—fundamentally change what communities see as the main problems with AI, yet global frameworks often ignore these differences.

Privacy, transparency, and fairness mean different things locally: For example, experts reframed privacy as a collective and relational concern rather than individual protection; transparency as building trust through accountability rather than technical disclosure; and fairness as ensuring equitable access rather than identical outcomes—suggesting that imposing Western definitions can miss what communities actually need.

Current governance leaves out local voices: The paper argues that ethical decision-making about AI is treated as a technical problem already solved by global experts, when it should instead involve ongoing negotiation with communities affected by these systems, redistributing who gets to decide what responsible AI looks like.

Context matters more than universal standards: Moving toward responsible AI requires treating ethical questions as ongoing and place-specific rather than settled rules, acknowledging that different regions face different infrastructural realities and moral priorities.