AI Is Ushering in a New Era of Colonialism

Why It Matters

The bias entrenched in AI reshapes public perception of identity and history, reinforcing systemic inequities and marginalizing non‑Western voices. Recognizing and correcting this data colonialism is essential for equitable technology development.

Key Takeaways

• LLMs train on Western data, reproducing dominant cultural biases.
• Indigenous oral traditions remain invisible to text‑based AI models.
• Data extraction without consent mirrors historic colonial resource exploitation.
• Big Tech prioritizes speed over inclusive data governance, deepening inequities.

Pulse Analysis

Artificial intelligence is reshaping how societies curate knowledge, but the underlying data pipelines echo the extractive logic of colonial empires. Researchers such as Julian Posada describe the “data grab” as a modern form of colonization, where corporations harvest cultural material from marginalized groups without permission, then repurpose it for profit. Because large language models are built primarily from sources generated in Western, educated, industrialized, rich, and democratic (WEIRD) societies, the resulting systems embed the worldview of those dominant cultures, marginalizing alternative narratives.

The bias manifests in everyday outputs that flatten cultural diversity. For instance, AI often describes Indian cuisine as uniformly “rich, aromatic, and spicy,” ignoring regional variations in spice blends and cooking techniques. Indigenous knowledge, which is traditionally transmitted orally, rarely appears in the text corpora that train these models, leaving entire worldviews invisible. When LLMs generate misinformation about history or identity, they not only misinform users but also reinforce stereotypes that have long been used to justify exclusionary policies.

Addressing AI‑driven colonialism requires a shift from speed‑centric development to data sovereignty frameworks. Companies can partner with Indigenous communities to co‑create datasets that respect cultural protocols and incorporate oral histories in a controlled manner. Policymakers should enforce consent‑based data collection standards and fund open‑source alternatives that prioritize multilingual, non‑Western sources. By embedding diverse epistemologies into model training, the industry can produce AI that reflects a broader spectrum of human experience, reducing bias and restoring agency to historically marginalized groups.