  AI Large Language Models (LLMs) like ChatGPT are set to reshape some aspects
of policymaking processes. Policy practitioners are already using ChatGPT for
help with a variety of tasks: from drafting statements, submissions, and
presentations, to conducting background research. We are cautiously hopeful
that LLMs could be used to promote a marginally more balanced footing among
decision makers in policy negotiations by assisting with certain tedious work,
particularly benefiting developing countries who face capacity constraints that
put them at a disadvantage in negotiations. However, the risks are particularly
concerning for environmental and marine policy uses, due to the urgency of
crises like climate change, high uncertainty, and trans-boundary impact.
  To explore the realistic potentials, limitations, and equity risks for LLMs
in marine policymaking, we present a case study of an AI chatbot for the
recently adopted Biodiversity Beyond National Jurisdiction Agreement (BBNJ),
and critique its answers to key policy questions. Our case study demonstrates
the dangers of LLMs in marine policymaking via their potential bias towards
generating text that favors the perspectives of mainly Western economic centers
of power, while neglecting developing countries' viewpoints. We describe
several ways these biases can enter the system, including: (1) biases in the
underlying foundational language models; (2) biases arising from the chatbot's
connection to UN negotiation documents, and (3) biases arising from the
application design. We urge caution in the use of generative AI in ocean policy
processes and call for more research on its equity and fairness implications.
Our work also underscores the need for developing countries' policymakers to
develop the technical capacity to engage with AI on their own terms.
