From data to guardianship: solving the “silent sample” dilemma in AI-powered biodiversity monitoring

In dense rainforests, an artificial intelligence (AI)-powered audio recorder can accurately identify the sounds of endangered species amidst a cacophony of insects and birds; over vast protected areas, drones equipped with computer vision models can detect poachers in real time. Artificial intelligence brings revolutionary hope to conservation biology, promising to guard Earth’s web of life with unprecedented precision and speed. The application of AI in natural resource management has a long history. Scholars explored the potential of expert systems and intelligent geographic information systems (GIS) in environmental management over three decades ago (Coulson et al., 1987), and research has continued into AI’s role in supporting land and ecosystem management decisions (Adamatti & Aguiar, 2010). However, when most AI models are trained in the relatively intact ecosystems of North America and Europe, and when high-cost monitoring equipment is densely deployed only in wealthy regions, a critical question emerges: will the world’s most biodiverse tropical rainforests, grasslands, and wetlands, often located in the Global South, become “silent samples” in the AI’s view due to “data poverty”? This imbalance in monitoring capability is not a neutral outcome of technology but a systemic risk that could exacerbate the global conservation gap.