Leveraging AI to improve evidence synthesis in conservation

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

doi:10.1016/j.tree.2024.04.007

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2024

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Cited by 5 publications

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Language barriers in conservation: consequences and solutions

Amano,

Berdejo-Espinola

2024

Trends in Ecology & Evolution

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Language barriers in conservation: consequences and solutions

Amano,

Berdejo-Espinola

2024

Trends in Ecology & Evolution

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The potential for AI to revolutionize conservation: a horizon scan

Reynolds,

Beery,

Burgess

et al. 2024

Trends in Ecology & Evolution

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Pressure to publish introduces large‐language model risks

Johnson,

Simmons,

Millard

et al. 2024

Methods Ecol Evol

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Large‐language models (LLMs) have the potential to accelerate research in ecology and evolution, cultivating new insights and innovation. However, whilst revelling in the plethora of opportunities, researchers need to consider that LLM use could also introduce risks. An important piece of context underpinning this perspective is the pressure to publish, where research careers are defined, at least partly, by publication metrics like number of papers, impact factor, citations etc. Coupled with academic employment insecurity, especially during early career, researchers may reason that LLMs are a low‐risk and high‐reward tool for publication. However, this pressure to publish can introduce risks if LLMs are used as a shortcut to game publication metrics instead of a tool to support true innovation. These risks may ultimately reduce research quality, stifle researcher development and incur reputational damage for researchers and the entire scientific record. We conclude with a series of recommendations to mitigate the magnitude of these risks and encourage researchers to apply caution whilst maximising LLM potential.

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Leveraging AI to improve evidence synthesis in conservation

Cited by 5 publications

References 52 publications

Language barriers in conservation: consequences and solutions

Language barriers in conservation: consequences and solutions

The potential for AI to revolutionize conservation: a horizon scan

Pressure to publish introduces large‐language model risks

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