Leveraging AI to improve evidence synthesis in conservation

Oded Berger-Tal*, Bob B.M. Wong*, Carrie Ann Adams, Daniel T. Blumstein, Ulrika Candolin, Matthew J. Gibson, Alison L. Greggor, Malgorzata Lagisz, Biljana Macura, Catherine J. Price, Breanna J. Putman, Lysanne Snijders, Shinichi Nakagawa*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

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 current biodiversity crisis. With the explosive growth of large language models (LLMs) and other forms of artificial intelligence (AI), we discuss here 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.

Original languageEnglish
Pages (from-to)548-557
Number of pages10
JournalTrends in Ecology and Evolution
Volume39
Issue number6
DOIs
Publication statusPublished - Jun 2024

Keywords

  • artificial intelligence
  • biodiversity conservation
  • evidence synthesis
  • large language models
  • systematic reviews

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