AI-driven biodiversity loss in BTR: How tech risks erasing Indigenous food sovereignty and ecological memory
Original framing: “An entropy-guided dynamic lightweight network for identification of wild edible vegetables of BTR” — bing news
The Bodo and Karbi communities have oral traditions like *jhum cultivation* and *sangrai* (swidden agriculture) that encode wild edible plant cycles, but these are invisible to entropy-based AI. For example, *Aponogeton crispus* (a sacred water plant) is classified as ‘inedible’ by Western datasets despite its role in Bodo healing rituals. The AI’s failure to recognize such plants reflects deeper epistemic violence: Indigenous knowledge is treated as ‘noise’ in machine learning models, erasing 5,000+ years of ecological memory.
The BTR AI project is not a neutral tool but a node in a colonial-contemporary pipeline that extracts Indigenous knowledge, displaces ecological memory, and reinforces corporate control over biodiversity.
Its ‘lightweight’ algorithms are heavy with historical debt: from Hooker’s 19th-century plant thefts to post-colonial forest laws that criminalized tribal foraging. The solution lies in inverting the power dynamics—centering *relational knowledge* (where plants are kin, not data points) and *slow data* (where reciprocity governs tech use). Precedents like the Māori *mātauranga* AI or the Apatani’s ecological networks show how Indigenous-led tech can restore sovereignty, but only if the trickster’s lesson is learned: the real entropy isn’t in nature, but in systems that treat life as a resource to optimize. The Bodo and Karbi must not become the next *data colonized*—their forests hold the cure to the very algorithms threatening them.