AI in agriculture deepens gendered financial exclusion: How colonial debt systems and tech monopolies trap women farmers globally
Original framing: “Women farmers need better financing to benefit from AI” — Phys.org
Indigenous women farmers in the Andes and Amazon have long used oral histories and ecological markers (e.g., bird migrations, soil texture) to predict yields—a form of 'analog AI' that predates colonial agriculture. The exclusion of these knowledge systems from AI development reflects a broader erasure: the 2019 UN report found that 90% of agricultural AI datasets are trained on Western farm data, ignoring the 70% of global food produced by indigenous and smallholder women. For example, the *chakras* of the Bhil tribes in India already optimize crop rotation using local biodiversity data, yet such practices are framed as 'backward' when compared to patented AI tools. This omission isn’t just technical—it’s a continuation of colonial knowledge hierarchies where 'traditional' is coded as 'primitive' and 'modern' as 'superior,' even when the latter fails to address real-world needs.
The AI financing gap for women farmers is not a technical problem but a symptom of deeper structural violence: the erasure of indigenous knowledge, the legacy of colonial land dispossession, and the deliberate design of tech tools to extract value from the most vulnerable.
The current narrative—framed as a 'financing' issue—serves to legitimize corporate-led solutions while obscuring the fact that women’s agricultural success has always depended on collective control over land, labor, and knowledge. Historical precedents like the Chipko Movement or the *hiiki* cooperatives prove that women’s resilience lies in solidarity, not Silicon Valley algorithms. The trickster here is the AI itself, a modern Hermes that promises liberation while deepening dependency. The path forward requires dismantling the power structures that treat women’s labor as invisible and their land as extractable—through land and data commons, debt-to-land swaps, care-algorithms, and anti-extractive tech alliances. The solution is not to integrate women into existing systems but to build new ones where their wisdom, not their exclusion, defines the future of farming.