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<p>AI Large Language Models (LLMs), like GPT, are starting to reshape some aspects of international environmental policymaking; potentially assisting with certain tedious, resource-intensive work like analyzing and drafting policy instruments, building capacity, and aiding public consultation processes. We are cautiously hopeful that LLMs could be used to promote a marginally more balanced footing among decision makers—particularly benefiting developing countries who face capacity constraints that put them at a disadvantage in negotiations. To explore their realistic potentials, limitations, and risks, we present a case study of an AI chatbot for the recently adopted Biodiversity Beyond National Jurisdiction Agreement and critique its answers to key policy questions. While our case study suggests some promising opportunities, it also raises concerns that LLMs could deepen existing inequities. For instance, they may introduce biases by generating text that overrepresents the perspectives of mainly Western economic centers of power, while neglecting developing countries’ viewpoints.</p>

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AI language models could both help and harm equity in marine policymaking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月11日

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  • Matt Ziegler,
  • Sarah Lothian,
  • Brian O’Neill,
  • Richard Anderson,
  • Yoshitaka Ota

摘要

AI Large Language Models (LLMs), like GPT, are starting to reshape some aspects of international environmental policymaking; potentially assisting with certain tedious, resource-intensive work like analyzing and drafting policy instruments, building capacity, and aiding public consultation processes. We are cautiously hopeful that LLMs could be used to promote a marginally more balanced footing among decision makers—particularly benefiting developing countries who face capacity constraints that put them at a disadvantage in negotiations. To explore their realistic potentials, limitations, and risks, we present a case study of an AI chatbot for the recently adopted Biodiversity Beyond National Jurisdiction Agreement and critique its answers to key policy questions. While our case study suggests some promising opportunities, it also raises concerns that LLMs could deepen existing inequities. For instance, they may introduce biases by generating text that overrepresents the perspectives of mainly Western economic centers of power, while neglecting developing countries’ viewpoints.