Artificial intelligence algorithms today reflect an inherent AI bias, representing the psychology and behavioral norms of just 12% of the global population. These artificial intelligence systems primarily broadcast the values of “WEIRD” societies—those in Western, Educated, Industrialized, Rich, and Democratic countries. When a user asks a neural network a question, the response may appear to come from a neutral computer, but it is actually filtered through the worldview of a resident of California or Western Europe, often ignoring the perspectives of the rest of the world.
What research reveals about AI bias in networks
A recent study from Harvard University titled “Which Humans” found that neural networks do not understand the majority of people on Earth. Researchers tested popular algorithms and discovered that their answers align with the opinions of Americans or Europeans 80% to 90% of the time. In contrast, the alignment with residents of Asia, Africa, or Latin America was only about 20%.
This discrepancy is particularly noticeable in psychological and domestic matters. When asked about family, duty, marriage, or tradition, AI almost always suggests an individualistic approach, such as “think of yourself first” or “defend your personal boundaries.” For traditional societies where collective support and respect for elders are prioritized, such advice often feels foreign.
The training paradox: Cheap labor and Western norms
The Western bias in neural networks is rooted in the training process. To ensure an AI does not produce nonsensical content, human reviewers manually evaluate its responses, marking good answers with likes and poor ones with dislikes.
Silicon Valley developers often outsource this routine labor to developing countries, such as Kenya, India, or the Philippines, to reduce costs. This creates a paradox: workers in lower-income nations filter texts based on strict instructions written by American executives. For low wages, they train the neural network to think and respond according to Western cultural standards.
Censorship and the language gap
AI developers frequently emphasize “safety,” training algorithms to block toxic or harmful content. However, the definition of “harmful” is often based solely on Western standards of political correctness. Any viewpoint that falls outside these parameters—even if it represents a standard conservative tradition in another country—is frequently blocked by the neural network.
AI also demonstrates significant linguistic discrimination. While complex questions in English receive deep and detailed responses, the same queries in Turkish, Arabic, or other languages often result in superficial, short, or overly cautious answers. Neural networks generally possess a weaker understanding of non-Western languages and cultures.
A new form of colonialism?
The promotion of standardized values serves a pragmatic business purpose. Artificial intelligence helps shape similar habits, desires, and mindsets in people worldwide. When individuals in any country begin to think like typical Western consumers, it becomes easier to sell them Western products, films, services, and subscriptions. In this capacity, AI functions as a tool that standardizes populations to meet the needs of large corporations.
The difficulty of creating independent AI
In response to these biases, some states and corporations are attempting to develop independent artificial intelligence. For example, the Chinese model DeepSeek filters information according to national regulations.
However, building a neural network from scratch is expensive, requiring billions of dollars and massive computing power. Consequently, many countries adapt free Western AI foundations and attempt to retrain them with their own rules. Because the base architecture remains Western, fully removing original biases is difficult. Ultimately, the values promoted by the technology tend to align with the entities that fund the server infrastructure.
