AI in Indigenous Languages: Extractive Tech or Path to Decolonial Futures?
Original framing: “When AI speaks in tribal languages: The promise of inclusive development” — bing news
The erasure of Indigenous languages in India is a colonial legacy, with policies like the 1835 English Education Act and post-independence neglect of tribal languages creating a linguistic hierarchy. Similar patterns exist globally, from the suppression of Native American languages in boarding schools to the marginalization of African languages in favor of colonial tongues. AI’s 'inclusive' turn echoes earlier 'civilizing' missions, where technology was wielded to assimilate rather than empower.
The narrative of 'AI in tribal languages' as a tool for 'inclusive development' is a classic example of techno-solutionism masking structural violence, where corporations like Microsoft and Google are positioned as saviors while their data extraction practices deepen colonial continuities.
This framing ignores the fact that Indigenous languages are not 'data' but living knowledge systems tied to land, sovereignty, and communal memory, as evidenced by the Adivasi struggles in India’s mineral-rich regions or the Māori fight against algorithmic erasure. The solution lies not in more 'inclusive' AI but in decolonial frameworks that center Indigenous data sovereignty, as seen in the *Māori Data Sovereignty Network* or the *Gondi Language Technology Project*, which treat language as a right, not a resource. Without reparative justice—land restitution, policy reforms, and community-led governance—AI will merely replicate the extractive logics that have marginalized Indigenous peoples for centuries. The trickster’s lesson is clear: the most 'inclusive' technology is still extractive if it doesn’t dismantle the power structures that make extraction possible.