Safeguarding Indigenous and Local Knowledge in the Digital Age

Recent legal challenges surrounding the use of copyrighted materials in training artificial intelligence (AI) systems have underscored the importance of data provenance, consent, and compensation. However, these debates largely overlook Indigenous and local knowledge systems, which are increasingly digitized and incorporated into AI training datasets without equivalent safeguards. Drawing on the recent Anthropic lawsuit, we examine ethical risks of extracting context-dependent, collectively held knowledge into biodiversity information systems. We argue that current practices risk detaching knowledge from its cultural, ecological, and community contexts, undermining long-standing ethical commitments within ethnobiology to reciprocity, consent, and stewardship. Building on frameworks such as the CARE Principles and the International Society of Ethnobiology (ISE) Code of Ethics, we propose that provenance in biodiversity and AI systems must extend beyond data sources to include knowledge holders, conditions of consent, terms of benefit, and terms of reuse.

AI training data · benefit sharing · biodiversity information systems · CARE principles · Consent · data provenance · indigenous knowledge · knowledge holders · local knowledge · Environmental and Cultural Studies in Latin America and Beyond · Ethics and Social Impacts of AI · Law, AI, and Intellectual Property