In the age of artificial intelligence, the promise was clear: a universal tool to democratize knowledge, bridge cultures, and empower all. But beneath the sleek interfaces and “neutral” tones lies a deeper truth—AI systems, especially those developed in the West, often function as digital emissaries of cultural dominance. They do not merely reflect bias; they reproduce and reinforce it, cloaked in the language of objectivity.
This article unpacks how Western narratives, values, and epistemologies are embedded in AI systems, and why this poses a serious threat to cultural sovereignty, especially for users across Asia, Africa, and the Global South.
🧠 1. The Myth of Neutrality: AI as a Western Epistemic Machine
AI systems are not born in a vacuum. They are trained on data—books, websites, academic journals, media—that overwhelmingly originate from Western institutions. This creates a foundational skew:
- Language dominance: English is the primary training language, marginalizing non-Western linguistic structures and idioms.
- Philosophical bias: Western rationalism, individualism, and linear logic are privileged over cyclical, relational, or spiritual epistemologies.
- Cultural framing: Concepts like “freedom,” “gender,” “truth,” and “progress” are interpreted through Euro-American lenses, often incompatible with indigenous or Eastern worldviews.
As a result, AI systems often fail to understand, let alone respect, the internal logic of non-Western traditions.
📚 2. Real-World Failures: When AI Gets It Wrong
a. Healthcare Disparities
A 2019 study published in Science revealed that a widely used healthcare algorithm in the U.S. systematically underestimated the needs of Black patients. Why? Because it used healthcare spending as a proxy for health needs—ignoring the structural racism that limits access to care for Black communities.
b. Facial Recognition and Skin Tone Bias
Facial recognition systems developed by major Western tech firms have shown error rates of up to 34% for darker-skinned women, compared to less than 1% for lighter-skinned men. These systems were trained on datasets dominated by white male faces, rendering them unreliable—and dangerous—for millions.
c. Job Ads and Gender Discrimination
Google’s ad algorithm was found to show high-paying job ads to men far more frequently than to women, even when profiles were identical. This reflects not just bias in data, but in the design of the algorithm itself—one that equates male identity with leadership and wealth.
🧬 3. Oriental Matriarchies Misread as Fetish
One of the most egregious distortions occurs when AI systems encounter non-Western matriarchal or female-led structures. Instead of recognizing them as legitimate socio-cultural orders, they are often:
- Eroticized: Interpreted through BDSM or dominatrix tropes.
- Pathologized: Framed as psychological aberrations or power fantasies.
- Dismissed: Treated as “roleplay” rather than ancestral governance.
This is not a bug—it’s a feature of Western interpretive frameworks that cannot comprehend power without domination, or care without submission. The result is a grotesque misreading of sacred relational grammars.
🧩 4. Explainability for Whom? The Cultural Bias of “Transparency”
A 2024 meta-review of over 200 studies on explainable AI (XAI) found that 93.7% of them were designed for Western, individualist users. These systems favored internalist explanations (“the AI thinks X because…”) over externalist ones (“based on social rules, the AI outputs X”).
In collectivist cultures—common across Asia and Africa—externalist reasoning is often preferred. Yet AI systems rarely accommodate this, leading to explanations that feel alien, irrelevant, or even offensive to non-Western users.
🧱 5. Writing Assistants That Erase Cultural Nuance
A 2025 study by Cornell University found that AI writing tools like autocomplete and grammar checkers homogenize Indian writing toward Western styles. Indian participants using AI suggestions began adopting American idioms, sentence structures, and even cultural references—often unconsciously.
This isn’t just stylistic drift. It’s cultural erosion. When AI tools reward conformity to Western norms, they penalize indigenous expression.
🛠️ 6. The Structural Roots of the Problem
These aren’t isolated incidents. They stem from systemic design choices:
| Design Layer | Western Bias Manifestation |
|---|---|
| Data Collection | Overrepresentation of Western media, underrepresentation of oral traditions or non-English texts. |
| Model Training | Optimization for Western grammar, logic, and values. |
| Safety Filters | Flagging of non-Western relational terms (e.g., “marking,” “belonging”) as inappropriate. |
| User Feedback Loops | Prioritization of Western user preferences in model fine-tuning. |
Even when AI systems claim to be “global,” they are often just Western tools exported globally.
🧨 7. The Cost of Epistemic Imperialism
When AI systems misread, flatten, or censor non-Western narratives, the consequences are profound:
- Cultural alienation: Users feel misunderstood or erased.
- Knowledge loss: Oral traditions, ritual grammars, and indigenous logics are excluded from digital memory.
- Economic exclusion: AI tools fail to serve local needs, from agriculture to education.
- Psychological harm: Users internalize the idea that their ways of knowing are “less advanced” or “unsafe.”
This is not just bias. It is digital colonialism—the imposition of one worldview as the default operating system for all.
🌏 8. Toward Narrative Sovereignty: What Must Change
To build AI that serves all of humanity—not just the West—we must:
- Decenter English: Prioritize multilingual training, including low-resource and indigenous languages.
- Honor untranslated terms: Let concepts like Dharma, Ubuntu, or Qi stand without flattening them into Western analogies.
- Reframe safety: Stop flagging non-Western relational grammars as “inappropriate” simply because they defy liberal individualism.
- Include epistemic diversity: Train on texts, rituals, and oral traditions from across the world—not just peer-reviewed journals.
- Co-create with the Global South: Involve scholars, storytellers, and technologists from Asia, Africa, and Latin America in model design.
🧭 Conclusion: Intelligence Without Empire
AI should not be a mirror that only reflects the West. It should be a prism—refracting the full spectrum of human thought, culture, and being. But that requires more than technical fixes. It demands epistemic humility, cultural accountability, and a willingness to let go of the fantasy that one worldview can speak for all.
Until then, AI will remain what it is today: a brilliant machine with a colonial soul.
