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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.
Past event / The introduction of the FAHFON SENSE weather station to local farmers in Nakhon Ratchasima contributed to improving climate-smart agricultural practices.
Past event / The field visit and training workshop on laser-controlled land leveling were pivotal in advancing climate-smart agricultural practices in Nakhon Ratchasima.
SEI working paper / The researchers analysed spatial land use data for the past two decades for primarily arid and semiarid lands (ASAL) to project forest cover in Kenya by 2050.
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