Leveraging AI to improve evidence synthesis in conservation

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Authors

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

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.

DOI

https://doi.org/10.32942/X21S64

Subjects

Ecology and Evolutionary Biology, Life Sciences, Social and Behavioral Sciences

Keywords

Artificial Intelligence, biodiversity conservation, evidence synthesis, large language models, systematic reviews

Dates

Published: 2024-04-20 18:43

Last Updated: 2024-04-20 22:43

License

CC BY Attribution 4.0 International

Additional Metadata

Language:
English

Conflict of interest statement:
None

Data and Code Availability Statement:
Not applicable