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The authors examine the potential of AI, particularly large language models, to accelerate and improve evidence synthesis in conservation, aiding in the more efficient management of the biodiversity crisis.
Photo: Chunyip Wong / Getty Images
Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and are used to support decisions and policies in a variety of applied fields, from medicine and public health to biodiversity conservation. However, conducting these exercises in conservation is often expensive and slow, which can impede their use and hamper progress in addressing the biodiversity crisis. With the explosive growth of large language models (LLM) and other forms of artificial intelligence (AI), the authors discuss the promise and perils associated with their use. They conclude that, when judiciously used, AI has the potential to speed up and hopefully improve the process of evidence synthesis, which can be particularly useful for underfunded applied fields such as conservation science.
Event on 17 September – 15 October / SEI launches a global event series with IPBES and IPCC Chairs, driving integrated nature and climate action for resilient societies across four continents.
Project / AgroMixNorth offers springboards for resilient cropping systems that provide nutritious food and income to farmers while increasing below-ground carbon storage.
Journal article / This perspective article examines the opportunities and risks of combined AI-EO technologies, focusing on their application in forestry.
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