How AI – and Increased Collaboration – Can Improve International Fisheries Monitoring

With demand for seafood rising worldwide and ocean health facing an array of threats, ensuring the sustainability of fisheries has never been more critical. Aside from national governments, that responsibility falls mostly to regional fisheries management organizations (RFMOs), which set catch limits along with rules on how, where and when fleets may fish and transfer catch on the high seas.

To guard against overfishing, RFMOs and their member countries must set those catch limits based on the best available science, monitor the activity of their fleets and work to improve compliance with the rules they have agreed upon.

Across the vast expanses of international waters, which in most cases begin 200 miles from the nearest shore, RFMOs face challenges in accomplishing those mandates. As just one example: RFMOs have long required human observer coverage on some vessels to collect critical data, but using people in this role can be dangerous and expensive.

Protect Marine Life Electronic Monitoring Programs

Article April 7, 2026

That’s why over the past decade, fisheries managers have begun looking for ways to supplement human observers with safer and more cost-effective alternatives. And some countries and RFMOs have found a solution in electronic monitoring (EM), in which cameras, sensors and other technology collect and transmit data from vessels to a review center for analysis. At the same time, governments and RFMOs are developing standards for the use of EM.

And now, the rapid development of artificial intelligence and machine learning (AI/ML) could be a massive boon to the development and uptake of EM, because computers and models can be trained to identify fishing activities happening onboard, reducing both the time and cost needed for people to review extensive video recordings and extract that information. But like with any new technology, adoption can be slowed because of a lack of familiarity from those responsible for implementing it.

So, since 2022, The Pew Charitable Trusts has worked to raise awareness of these emerging technologies; grow relationships among governments, EM providers, AI developers, civil society and industry; and build a common terminology and understanding of the field. Ultimately, these collaborations are helping to expand the uptake of EM across commercial high seas fisheries.

Collaboration leads to best practices and science advances

In 2023 and 2024, Pew convened more than 30 data scientists, technology providers and policymakers for the first and second Global Artificial Intelligence in Fisheries Monitoring summits. The participants discussed how to design AI/ML systems to complement the use of existing human observers and provide new job opportunities, while also identifying barriers to expanding use of the technology in fisheries management. They also discussed ways in which governments could better encourage the development and use of AI and ML to monitor their fisheries and the need to standardize how managers should evaluate AI performance. Participants also recognized the critical need to make data more open and accessible in order to better train AI models and improve functionality and confidence in AI-assisted EM.

The symposiums not only resulted in substantial new dialogue around how to improve fisheries management through AI/ML and emerging technologies, but they led to the creation of an AI/ML glossary for officials, scientists and others involved in the fishing industry. Based in part on the symposiums, fisheries managers and businesses are developing even more practicable guidance. For example, New England Marine Monitoring, a fisheries technology company, has developed best practices for designing EM so that it can incorporate AI.

All of these developments, coupled with new research that has emerged since the summits, is driving the conversations about AI forward. In 2024, a report in the ICES Journal of Marine Science addressed how information sharing and partnerships among governments, fishers, vessel owners and technology service providers can advance AI systems and help meet monitoring and sustainability goals. And new pilot projects are testing these technologies on the water, demonstrating that AI can be used to support near real-time counting of catch, identify fish species and monitor working conditions onboard fishing vessels.   

As EM moves beyond pilot projects and into active integration with observer coverage programs, RFMOs, fishery managers, scientists and fishers must prepare for the inclusion of AI/ML in fisheries monitoring. Since Pew’s first summit more than two years ago, technological advances and new research have made a pivot to AI even more likely. As this field continues to advance, policymakers should embrace constructive collaboration, open data sharing and pragmatic investment in these tools to improve the collection and analysis of fishing data across the world’s ocean.

Jamie Gibbon leads The Pew Charitable Trusts’ efforts to improve compliance and increase monitoring of fisheries on the high seas.