Artificial Intelligence and Ethnocentrism: An Anthropological Perspective

Introduction

This brief lab note examines the theoretical and conceptual intersections between Artificial Intelligence (AI), ethnocentrism, and cultural relativism. As AI systems increasingly mediate global communication, knowledge production, and cultural interaction, a fundamental question arises: can these technologies embody cultural neutrality, or do they propagate ethnocentric biases rooted in Western epistemologies? Drawing on foundational anthropological concepts and contemporary scholarship, this text argues that AI is not culturally neutral but rather shaped by the historical and political structures in which it is developed. It concludes with recommendations for designing AI systems informed by cultural relativism, decolonial theory, and inclusive ethics.

1. Why Anthropology Must Engage AI

The digital age has introduced complex sociotechnical systems that challenge traditional anthropological boundaries. Among the most transformative of these systems is Artificial Intelligence (AI), whose influence spans diverse domains, including healthcare, education, governance, and warfare. While these technologies are often implied as objective, value-neutral tools, anthropology reminds us that all technologies are embedded in cultural and political contexts (Pfaffenberger 1992; Suchman 2007).

Here is the fundamental question:

Can AI reflect cultural relativism, or is it structurally predisposed toward ethnocentrism?

As AI systems interact with people across diverse cultural settings, anthropologists must scrutinize not only what AI does but also how it encodes and disseminates particular worldviews. Understanding this dynamic is essential for imagining a future in which technology respects, rather than erases, cultural difference.

2. Conceptual Foundations: Ethnocentrism and Cultural Relativism

2.1 Ethnocentrism: A Historical Overview
Ethnocentrism, defined as the belief in the superiority of one’s own culture, has long been recognized as a cognitive and political orientation with profound consequences (Sumner 1906). Ethnocentrism manifests in the imposition of cultural norms, linguistic hierarchies, and moral judgments on others. In technological contexts, this often appears as the universalization of Western ideals such as rationalism, secularism, and liberal individualism.

2.2 Cultural Relativism: An Anthropological Response
As a counterpoint, cultural relativism emerged from the work of Franz Boas and his followers, who argued that cultures must be understood within their own historical and environmental contexts (Boas 1940; Benedict 1934). Cultural relativism promotes epistemological humility and pluralism, recognizing the legitimacy of diverse moral and ontological systems. In the context of AI, this principle demands systems that are responsive to local cultural logics, rather than imposing abstract, supposedly universal standards.

3. How AI Encodes Ethnocentrism

3.1 Biased Training Data and Epistemic Dominance
AI systems learn from data. However, the vast majority of training data comes from online sources that are disproportionately Anglo-European and urban in nature. Studies have shown that large language models (LLMs), such as GPT and BERT, are trained on corpora that reflect Western-centric worldviews (Bender et al., 2021). This results in AI systems that internalize and reproduce biases, favoring dominant languages, cultures, and epistemologies.

3.2 Epistemological Monocultures
Kate Crawford (2021) argues that AI is not just a set of tools, but a regime of knowledge production shaped by extractivism, coloniality, and capitalist rationality. The epistemic assumptions of AI—such as objectivity, standardization, and predictability—are themselves culturally situated. As Suchman (2007) notes, the ideal of an autonomous, neutral machine reflects specific Euro-American ideas about intelligence and agency.

3.3 Algorithmic Universalism and Moral Assumptions
Even when AI is applied globally, it tends to enforce culturally specific norms. For example, content moderation algorithms on platforms like Facebook and YouTube often flag indigenous spiritual content or non-Western gender expressions as violations of policy. As Benjamin (2019) demonstrates in her concept of the “New Jim Code,” technological systems can amplify existing racial and cultural hierarchies under the guise of neutrality.

4. The Limits of Cultural Neutrality in AI

4.1 Cultural Neutrality as a Myth
The idea that AI can be culturally neutral is increasingly discredited. As Eubanks (2018) and Noble (2018) illustrate, technologies inevitably reflect the interests, values, and assumptions of those who design and fund them. AI systems encode not only linguistic and cultural content but also frameworks for interpreting human behavior, morality, and social order.

4.2 Multilingualism and Semiotic Diversity
While some progress has been made in incorporating multiple languages into AI systems, the majority of linguistic diversity remains unrepresented. There are over 7,000 languages spoken globally, yet major AI platforms expressively support fewer than 100. As Bird (2020) warns, the marginalization of minority languages in AI tools contributes to digital language death and epistemic injustice.

4.3 Ontological Pluralism vs. Technical Efficiency
Different cultures have distinct ontologies—ways of understanding reality, life, and the human. AI, however, often reduces complexity into predefined categories, ignoring plural ontologies. For instance, AI systems might struggle to comprehend the relational personhood in many African or Indigenous worldviews, where identity is co-constituted through community and cosmology (Watene 2016).

5. Toward a Culturally Relativistic AI

5.1 Participatory Design and Community-Led Development
Participatory design invites marginalized communities to shape the development of AI systems. As Escobar (2018) argues in his work on pluriversal design, alternative futures must be co-constructed with communities rather than imposed upon them. Participatory models could help ensure that AI reflects the needs, values, and epistemologies of diverse users.

5.2 Polycentric and Localized AI Systems
Rather than seeking universal solutions, AI should be polycentric—designed with multiple cultural centers in mind. This could mean training localized models in indigenous languages, or building ethical frameworks that draw from non-Western philosophies such as Ubuntu (Metz 2010) or Confucianism (Wong 2021). Local data sovereignty and context-sensitive adaptation are essential.

5.3 Ethical Reflexivity and Transparent Assumptions
AI systems must make their assumptions visible. This includes revealing the cultural biases in training data, the normative values embedded in algorithms, and the power structures that shape development. Anthropologists can contribute to this reflexive work by offering critical ethnographies of AI production and use (Ames 2018).

6. Conclusion: Anthropology’s Role in AI Futures

Anthropology brings essential tools to the ethical and political analysis of AI. By foregrounding cultural difference, relationality, and reflexivity, anthropologists can help design AI systems that do not merely replicate dominant norms but open space for multiple ways of being and knowing. AI must not become a new vector of cultural imperialism. Instead, it can become a platform for intercultural dialogue, provided it is approached with humility, pluralism, and fairness.

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References

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