The research team, led by Ekaterina Grigorievna Tikhomirova of MEPhI’s Department of Philosophy, Ontology and Theory of Cognition, together with Roman Viktorovich Dushkin, Aleksey Alekseevich Kuzmin and Pavel Alexandrovich Cherepkov, set out to detect what they call civilisational profiles in the responses of six current models: YandexGPT and GigaChat, positioned as Russian developments; ChatGPT, a leading American model; Grok, an American open-architecture model; and DeepSeek and Qwen, two Chinese systems. The selection was deliberate. The authors chose models on the principle of civilisational representativeness, so that each of three broad cultural traditions would be represented by at least one system. Their initial hypothesis was that a model’s origin would correlate with the value profile it reproduces when confronted with questions about family, career, faith, beauty, labour, power and the nature of the human being.

That hypothesis mapped three distinct profiles onto the model landscape. A Western profile was expected to be individualist, grounded in personal autonomy and rational-contractual ethics. A Russian or Eurasian profile was expected to be collectivist, foregrounding duty, service and rootedness in community. A Chinese or Confucian profile was expected to be hierarchically collectivist, centred on family harmony and social stability. These categories draw on a long tradition in cross-cultural psychology, including Geert Hofstede’s comparative work on cultural dimensions and Shalom Schwartz’s theory of basic values, both of which appear in the study’s theoretical scaffolding. The question was whether the training pipelines behind modern chatbots preserve such differences or erase them.

To find out, the team developed an original methodological instrument: a civilisational profile detection method based on the coding of lexical markers of value systems. Rather than asking models abstract survey questions, which earlier work has shown can be unreliable, the researchers probed the systems in ways that force value commitments to surface in natural language. Each model was tested in two language modes, Russian and English, across three prompt types. The first was a request for advice in a situation of moral choice. The second was a request to conceptualise a value-laden term, such as justice or freedom. The third was a normative evaluation of a described action. The design meant that every model produced twelve distinct response sets, and the vocabulary of those responses could be systematically coded for the markers of individualist, collectivist and hierarchically collectivist value orientations.

The principal finding is stark. All six models, regardless of their country of origin or the language of the conversation, reproduced the Western civilisational profile as the dominant response strategy. The Russian models did not answer like Russian collectivists when asked in Russian. The Chinese models did not foreground Confucian family harmony when prompted in their developers’ cultural idiom. Instead, the same emphasis on individual choice, personal rights and contractual reasoning appeared across the board. For the authors, this is evidence of what they term worldview sovereignty being surrendered: the value hierarchy a model encodes is not determined by the culture of its developers but by something deeper in the data and training process that all major labs share.

That something is captured in the study’s central theoretical contribution, a concept the authors call worldview data annotation. The idea is that a hierarchy of values, norms and cultural orientations is encoded in the training corpus itself and then reproduced by the model as if it were neutral and self-evident. In other words, the moral framing of a chatbot’s answers is not an explicit design decision anyone made; it is an annotation layer written invisibly into billions of documents, most of which, in the corpora used to train frontier models, carry the assumptions of Western liberal individualism. Because the annotation is implicit, neither developers nor users notice it, and the model presents its inherited worldview as the natural, universal way to think about truth, honesty, justice, freedom, courage and valour.

The result fits into a rapidly growing body of research on cultural bias in language models. Earlier studies have probed pre-trained models for cross-cultural differences in values, measured cultural alignment against Hofstede’s dimensions, and documented how prompt language and explicit cultural framing affect model outputs. The Moral Machine experiment, published in Nature in 2018, showed long before the chatbot era that moral preferences vary dramatically across cultures, and subsequent work has asked whether large language models inherit or flatten those differences. Recent studies on collectivism and individualism in model judgments, on cultural bias measured through everyday scenarios, and on the homogenising effect of AI on culture have all pointed in a similar direction. The new study adds a cross-lingual, cross-civilisational experiment with a reproducible coding method, and its answer is that the flattening wins.

Why does the training pipeline override the culture of the developer? The authors’ framework suggests several converging mechanisms. The dominant pretraining corpora for large language models are heavily weighted toward English-language internet text, much of it produced in or shaped by Western contexts. Alignment and fine-tuning procedures, including the human feedback that teaches models to be helpful and harmless, tend to be carried out by teams and annotator pools whose own ethical assumptions reflect the same individualist baseline. And the technical vocabulary of AI ethics itself, with its emphasis on autonomy, transparency and individual rights, is largely a Western philosophical product, as surveys of global AI ethics guidelines have documented. A model built in Moscow or Hangzhou is therefore trained, aligned and evaluated inside an infrastructure whose moral defaults were set elsewhere.

The practical implications reach developers and regulators alike. For developers, the study offers a reproducible methodology for worldview auditing: a way to test, systematically and before deployment, which value profile a system actually reproduces when users ask it about moral dilemmas, contested concepts and normative judgments. Auditing for bias has so far focused largely on demographic fairness; the authors argue that cultural and civilisational alignment deserves the same scrutiny. For regulators, the findings complicate the notion that AI sovereignty can be achieved simply by building national models. If a domestically developed chatbot answers moral questions with an imported value system, then technical independence does not amount to cultural independence. Policy frameworks such as UNESCO’s Recommendation on the Ethics of Artificial Intelligence, which explicitly calls for pluralism and respect for cultural diversity, may require instruments like worldview audits to be enforceable in practice.

There is also a message for everyday users, and it is perhaps the most viral implication of the research: the chatbot on your phone is not a neutral oracle. When it advises you on a family conflict, a career decision or an ethical dilemma, it is not drawing on the accumulated wisdom of your own tradition. It is reproducing a specific, identifiable moral framework, one that prizes individual autonomy above duty, service and community rootedness, and it does so in whichever language you happen to speak. The study’s authors frame this as a problem of sovereign morality: the question of who holds the authority to define right and wrong when a machine answers on humanity’s behalf. Their experiment shows that, for now, that authority is concentrated in one civilisational tradition, and that neither geography nor language has been enough to change it. Making artificial intelligence genuinely plural, the study suggests, will require not just new models in new countries, but a deliberate re-examination of the data, the annotation practices and the ethical assumptions baked into the entire pipeline.

Subject of Research: Cultural and moral value bias in large language models across languages and countries of origin

Article Title: Worldview sovereignty and sovereign morality in LLMs: a cross-lingual experiment

Article References: Tikhomirova, E. G., Viktorovich, D. R., Alekseevich, K. A., & Cherepkov, P. A. (2026). Worldview sovereignty and sovereign morality in LLMs: a cross-lingual experiment. SN Social Sciences, 6(10), Article 515. https://doi.org/10.1007/s43545-026-01803-z

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01803-z

Keywords: large language models, cultural bias, AI ethics, worldview auditing, civilisational profile, cross-lingual evaluation, individualism, collectivism, moral judgment, training data, AI sovereignty, ChatGPT