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How Artificial Intelligence can help conserve biodiversity

Feb. 22, 2023.
1 min. read Interactions

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Lewis Farrell

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Highly curious about things that increase my awareness, expand my perception, and make me open to being a better person.

The International Telecommunication Union (ITU) hosted a webinar to discuss the potential of artificial intelligence (AI) in supporting the detection and attribution of biodiversity change, as well as its role in implementing a biodiversity conservation framework. The AI for Good Discovery Series on AI for Biodiversity, co-organized with the Convention on Biological Diversity (CBD), seeks to bring together the AI and biodiversity communities in order to protect the natural world.

CBD Acting Executive Secretary David Cooper emphasized the importance of AI in identifying species, drivers of biodiversity loss, and mapping suitable areas for ecosystem restoration. He did, however, emphasize the importance of human intelligence in ensuring that AI does not disregard local knowledge or violate the rights of stakeholders and rights holders, including Indigenous Peoples and local communities.

McGill University’s Andrew Gonzalez proposed a framework for the detection and attribution of biodiversity change, as well as the potential of AI in tracking progress and linking action on drivers to biodiversity outcomes. He emphasized the importance of theory-guided data science, which would combine big and small data to guide AI applications.

Bottlenecks in advancing AI in the detection and attribution system were discussed, including AI literacy, data availability, and the disconnect between the human, the analytical tool, and the inferences made. Participants endorsed public-private partnerships to leverage tools for biodiversity change, peer-to-peer data sharing, and combining causal inference frameworks in machine learning with traditional and theoretical knowledge.

AI has the potential to play a significant role in biodiversity conservation; however, it must integrate human intelligence, combine big and small data, and address existing bottlenecks in AI advancement.

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