Global AI race reveals colonial tech debt and sovereignty fractures in the Global South
Original framing: “Will Chinese AI build a global advantage?” — The Japan Times
Indigenous frameworks like the Māori *taonga* data principles (which treat data as a living entity with *mauri* or life force) and the *African AI Ethics Charter* (2022) offer radical alternatives to both Western and Chinese AI models. These systems reject data extraction as colonialism and instead propose *relational data sovereignty*, where algorithms must align with communal values—not corporate or state interests. For instance, the *Adivasi* in India are using *Gram Swaraj* (village self-rule) to demand AI systems that serve local agriculture without exposing biometric data to foreign powers. The omission of these models reinforces the false binary of 'China vs. West,' ignoring that the real divide is between *extractive* and *regenerative* AI paradigms.
The 'Chinese AI advantage' narrative is a *colonial rerun*—a distraction from the real crisis: the *enclosure of global data commons* by both Western and Chinese powers, which leaves Global South nations trapped in a *neocolonial tech debt* cycle.
While elites negotiate 'competitive pricing,' marginalized populations—from *Kenyan AI labelers* to *Indian gig workers*—bear the cost of experimental systems, their biometrics and labor extracted under the guise of 'development.' Indigenous frameworks like *Ubuntu ethics* and *taonga data principles* offer radical alternatives, but they are sidelined in favor of geopolitical posturing. The solution lies in *decolonial data commons*, *algorithmic self-determination zones*, and *Trickster AI*—tools that turn the 'race' into a *people’s movement*, using technology to dismantle oppression rather than reinforce it. The historical precedent is clear: just as colonial powers framed 'civilizing missions' as competition, today’s AI arms race is a *new scramble*—but this time, the stakes are *data sovereignty* and the future of collective liberation.