Can AI Accelerate Biodiversity Conservation While Avoiding Data Colonialism?

A new study in Trends in Ecology & Evolution identifies 21 key AI applications for conservation alongside critical implementation risks...

Artificial intelligence is reshaping conservation at an unprecedented scale and speed. A project-funded horizon scan published in Trends in Ecology & Evolution identifies 21 key AI applications likely to benefit biological conservation alongside critical implementation risks. The urgency is real, biodiversity loss is accelerating globally, and AI tools could accelerate conservation progress if deployed responsibly. But responsible deployment requires addressing fundamental questions about power, equity and who benefits from conservation innovation.

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AI's conservation toolkit: from species recognition to ecosystem modelling

Deep learning can reveal 'dark diversity'—overlooked species present in ecosystems but rarely encountered—expanding understanding beyond conspicuous organisms. Multimodal models improve biodiversity loss predictions, identifying where conservation interventions would have the greatest impact. Real-time monitoring of wildlife trade identifies smuggling patterns and enforcement priorities. Drone imagery analysed by neural networks maps habitat change at unprecedented spatial resolution, revealing landscape transformation impossible to track using traditional methods.

Automated species identification processes camera-trap images at scales previously requiring months of human effort, accelerating continuous, long-term biodiversity monitoring. Acoustic systems paired with AI detect nocturnal species across entire forest reserves, revealing cryptic species independent of human field visits. Advanced algorithms can even predict which species may emerge in future climate scenarios, helping conservation planners anticipate ecosystem shifts. The technological toolkit spans molecular genetics to landscape ecology, offering applications across conservation's full spectrum.

The equity challenge: sovereignty, resources and knowledge

Yet these tools carry serious risks. Data access and sovereignty issues arise particularly in the Global South, where biodiversity concentrates. Wealthier nations and institutions risk monopolising access to biodiversity data and AI-derived insights, perpetuating inequalities in conservation capacity and decision-making power. Computational resources required for intensive analysis are unequally distributed globally.

Training data bias means algorithms perform poorly in under-represented regions or for neglected species, limiting applicability to priority conservation areas. Concerns about 'AI colonialism' reflect fears that conservation intelligence could be extracted from developing nations, processed in wealthy countries and sold back as products without local benefit or consent.

The researchers emphasise that technology alone cannot solve conservation problems. Human expertise, local knowledge and on-the-ground work remain essential. Rushing AI deployment risks displacing these vital elements. Development of practitioner skills could suffer if communities rely excessively on automated systems without understanding underlying ecological processes. The authors call for deliberate governance frameworks ensuring that AI development prioritises equity and local benefit-sharing rather than extractive practices.

Building equitable AI conservation

Communities of practice where practitioners share experiences and learn collectively help translate emerging technologies into practical conservation impact. Responsible AI deployment requires deliberate attention to data sovereignty, local ownership of conservation intelligence and equitable benefit-sharing throughout AI system development and deployment.

This means co-designing AI tools with communities rather than imposing solutions from outside. It means ensuring that conservation data benefits the communities that generated it, not just distant institutions.

Read the full paper: https://doi.org/10.1016/j.tree.2024.11.013. For more FRAMEwork publications visit https://www.framework-biodiversity.eu/publications.