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.

Against a backdrop of rapidly expanding health artificial intelligence (AI) development, this paper examines how the European Union’s (EU) stringent digital regulations may incentivise the outsourcing of personal health data collection to low- and middle-income countries (LMICs), fuelling a new form of AI ethics dumping. Drawing on parallels with the historical offshoring of clinical trials, we argue that current EU instruments, such as the General Data Protection Regulation (GDPR), Artificial Intelligence Act (AI Act) and Medical Devices Regulation, impose robust internal safeguards but do not prevent the use of health data collected unethically beyond EU borders. This regulatory gap enables data colonialism, whereby commercial actors exploit weaker legal environments abroad without equitable benefit-sharing. Building on earlier EU responses to ethics dumping in clinical trials, we propose legal and policy pathways to prevent similar harms in the context of AI.

This article examines the challenges Indigenous communities face in safeguarding their intangible cultural heritage (ICH) in the digital age, using two case studies. Referring to the Te Hiku Media case, it analyzes the threat of data colonialism posed by corporate digitization projects. The article argues that existing legal frameworks provide limited protection for Indigenous ICH, prompting Indigenous communities to develop the innovative theory of Indigenous data sovereignty (ID-SOV). The Government of Nunavut–Microsoft partnership case highlights the benefits and drawbacks of public–private partnerships (PPPs) for Indigenous ICH. Key takeaways from both cases’ analysis lead to our proposal of integrating ID-SOV principles into PPPs to limit data colonialism risks and improve the sustainability of Indigenous ICH digitization projects. The article contends that implementing ID-SOV principles by design and by default in PPPs can empower Indigenous communities while leveraging the oversight of public actors and resources of private partners to safeguard Indigenous ICH through digital tools.