Limitations of the Linnaean categorization model in the age of AI

Linnaean taxonomy, which imposes hierarchical classifications based on morphological characteristics, has become deeply embedded in modern data architecture, from databases to metadata schemas to AI training datasets. With its hierarchical structure and rigid categorization, Linnaean taxonomy privileges one type of knowledge while marginalizing alternative taxonomies that offer more fluid, contextual, and relational understandings of the natural world. This paper examines how the legacy of Linnaean taxonomy continues to shape contemporary classification systems and artificial intelligence (AI). Indigenous knowledge systems, which include spiritual, cultural, and ecological dimensions, view entities not as isolated objects but as nodes in dynamic, interconnected networks. We draw from the French naturalist, Comte de Buffon, who, in line with Indigenous knowledge systems, viewed nature as continuous and contextual rather than discretely compartmentalized. The dominance of Linnaean-style classification in AI and data systems perpetuates colonial power dynamics and contributes to knowledge homogenization while losing Indigenous languages and classification systems crucial for addressing contemporary environmental challenges, particularly in agriculture and biodiversity conservation. In this Age of AI, we call for a holistic and ecological approach to archives. Therefore, we propose applying ‘rhizomatic hylomorphism,’ an ethnobiological, alternative classification that transcends hierarchical taxonomies to embrace multiplicity, relationality, and contextual meaning.