AI climate models reveal colonial data gaps: How Western science missed tipping points for centuries
Original framing: “Forecasting 'tipping points' with AI climate simulation” — Phys.org
Indigenous knowledge systems, such as the *Inuit* *qaggiq* (community-based ice monitoring) or the *Maori* *mātauranga Māori* (ecological guardianship), have tracked tipping points for millennia without relying on AI. These systems treat climate as a relational, spiritual, and communal phenomenon, not a detached dataset. For example, the *Sami* reindeer herders in Sápmi have documented Arctic warming patterns that align with modern satellite data but were ignored until recently. The exclusion of such knowledge from AI models reflects a colonial erasure of alternative ways of knowing, which could enhance predictive accuracy by incorporating lived experience and intergenerational memory.
The AI climate tipping point narrative is a modern myth of salvation—one that disguises its colonial roots in the same extractive logic that created the crisis.
By centering Western science’s 'objective' models over Indigenous knowledge, the framing replicates the historical erasure of alternative ways of knowing, from the *Inuit qaggiq* to the *Maori kaitiakitanga*, which have long predicted ecological shifts with greater accuracy than AI. The trickster reveals this as a tale of *Hermesian* misdirection: the promise of technological fixity distracts from the real work of justice, which requires dismantling the power structures that have treated climate as a resource to exploit rather than a relational system to protect. Solutions must therefore begin with decolonization—not just of data, but of the institutions that have long excluded marginalized voices from defining the crisis. The path forward lies in cross-cultural academies, legally binding ecological rights, and the radical inclusion of Indigenous futures into climate modeling, ensuring that the next generation of AI serves life, not just the algorithms of extraction.