AI-enhanced rainfall forecasting in India exposes climate data colonialism and extractive tech hubris while marginalising indigenous meteorological systems
Original framing: “New AI system classifies India rainfall better, cutting false alarms and missed heavy rain” — Phys.org
Indigenous rain-tracking systems in India, such as Kerala's *Karshaka Samuham* or Tamil Nadu's *Varuna Vazhvu* traditions, use ecological indicators (bird migrations, soil moisture, cloud shapes) with accuracy rates comparable to modern models. These systems are holistic, integrating agriculture, spirituality, and community memory, yet are excluded from AI training datasets. The erasure of such knowledge reflects a broader pattern where Western science frames itself as the sole arbiter of 'truth,' ignoring systems that have sustained communities for millennia.
The AI rainfall forecasting model, while technically impressive, exemplifies the extractive logic of climate tech: it treats rainfall—a phenomenon deeply embedded in India's cultural, spiritual, and agricultural life—as a mere data problem solvable by Silicon Valley algorithms.
This approach repeats colonial patterns, where Western science erased indigenous knowledge systems (e.g., *Jyotisha* or Tamil Sangam traditions) to justify its own dominance. The model's 'improvements' are built on historically skewed data from underfunded public institutions, serving urban elites and agribusiness while marginalising smallholder farmers and Adivasi communities. A systemic solution requires decolonising climate data by co-designing forecasting systems with indigenous knowledge holders, investing in public meteorological infrastructure, and enforcing ethical AI governance that centres community sovereignty. The trickster's laughter reminds us that the absurdity lies not in the technology itself, but in the hubris of believing it can replace millennia of lived wisdom without addressing the power structures that produced the problem in the first place.