AI's Fluency In Other Languages Hides Western Worldview

Beneath the surface of these multilingual conversations, English functions as a hidden intermediary. A study by researchers at the University of Oxford found that LLMs routinely conduct their core reasoning in English, even when prompted in other languages. They translate the output at the final stage. A user receives flawless text in their preferred language, but the underlying logic originates elsewhere.

To examine how this plays out in practice, I ran experiments with ChatGPT, Claude and Gemini. I asked questions in both English and Indonesian about concepts such as education, responsibility, well-being and several Indonesian terms that resist direct translation into English. These included terms such as“gotong royong,” which describes a tradition of communal mutual assistance.

Then I asked questions about education in both languages, using the word pendidikan in Indonesian. The answers were consistently centered on individual development, personal autonomy, critical thinking and preparation for the labor market.

What largely disappeared were the dimensions of pendidikan that Indonesian educational traditions have historically emphasized. In Indonesia education has long been focused on ethical discipline. Scholars of Indonesian education such as Christopher Bjork and Robert Hefner have documented how distinct these traditions are from models that treat education primarily as a path to individual advancement and career preparation, which is the lens through which the AI tools viewed education.

The Indonesian concept of malu offers a starker example. Often translated as“shame” or“embarrassment,” malu has been analyzed by anthropologists Clifford Geertz and Tom Boellstorff as something closer to a shared social awareness.

A person might feel malu when speaking out of turn in front of elders, or when a family member's behavior reflects poorly on the household. It regulates conduct and signals awareness of one's position within a web of relationships.

It is cultivated, not merely felt. It is a form of relational awareness rather than a private psychological event.

When asked directly to define malu, the models acknowledged its social dimensions. In scenario-based questions that simply used the word without asking for a definition, however, all three fell back on the English translation of shame, consistently framing it as an individual emotional experience.

One representative response framed malu as a normal emotional reaction to be managed through self-reflection and confidence-building – a personal psychological problem rather than a social one. The relational dimensions of the concept disappeared entirely, replaced by the language of individual emotional regulation.

A distinctly American worldview travels inside the translation, largely unannounced.

Translation is far cheaper: Train one model on the vast English-language web, then use multilingual output capabilities to serve global markets. As media scholar Safiya Umoja Noble argues about algorithmic systems more broadly, what looks like a technical outcome is actually a structural one, shaped by who has the wealth and infrastructure to build these systems.

The embedded worldview isn't a mistake; it's what happens when knowledge production is profit-seeking.

The main exceptions are Chinese models such as DeepSeek and Alibaba's Qwen. They represent a genuine alternative to the US-dominated pipeline, though research shows they operate through a distinctly Chinese cultural lens. Asked about a workplace disagreement, for instance, they tend to advise silence or indirect phrasing to preserve harmony rather than the direct, private correction that Western models recommend.

Other regional efforts, such as SEA-LION for Southeast Asia and Kan-LLaMA for the Indian language Kannada, use US models as their foundation. They add additional vocabulary and cultural information related to local languages. But the core logic remains tied to the original U.S. training.