Leveraging AI to improve evidence synthesis in conservation

Abstract

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), we discuss the promise and perils associated with their use. We 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.