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New opportunities and challenges for conservation evidence synthesis from advances in natural language processing

Published Article

Global

Publication date: April 1, 2025

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Conservation challenges demand rapid translation of scientific evidence, yet interdisciplinary synthesis is increasingly difficult as research output grows. This study reviews how natural language processing, machine learning and AI can automate literature searches, process unstructured text and extract key parameters to accelerate evidence synthesis in conservation social science. These tools can scale data categorization and support cross‑disciplinary integration, but major questions remain about how to design ethical, effective hybrid AI‑expert systems. The work highlights opportunities for AI‑enabled evidence synthesis while emphasizing the need for responsible implementation.

Subject Tags

  • Data Science and Artificial Intelligence
  • Social Sciences

Abstract

Addressing global environmental conservation problems requires rapidly translating natural and conservation social science evidence to policy-relevant information. Yet, exponential increases in scientific production combined with disciplinary differences in reporting research make interdisciplinary evidence syntheses especially challenging. Ongoing developments in natural language processing (NLP), such as large language models, machine learning (ML) and data mining, hold the promise of accelerating cross-disciplinary evidence syntheses and primary research. The evolution of ML, NLP, and artificial intelligence (AI) systems in computational science research provides new approaches to accelerate all stages of evidence synthesis in conservation social science. To show how ML, language processing, and AI can help automate and scale evidence syntheses in conservation social science, we describe methods that can automate querying the literature, process large and unstructured bodies of textual evidence, and extract parameters of interest from scientific studies. Automation can translate to other research agendas in conservation social science by categorizing and labeling data at scale, yet there are major unanswered questions about how to use hybrid AI-expert systems ethically and effectively in conservation.

Citation

Chang, C. H., Cook‐Patton, S. C., Erbaugh, J. T., Lu, L., Masuda, Y. J., Molnár, I., ... & Robinson, B. E. (2025). New opportunities and challenges for conservation evidence synthesis from advances in natural language processing. Conservation Biology, 39(2), e14464. https://doi.org/10.1111/cobi.14464

TNC Authors