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Artificial Intelligence Working Group

The Ad-hoc Group on Artificial Intelligence supports the coordinated and responsible integration of AI and machine learning across the EuroGOOS community, promoting data quality, transparency, and collaboration to enhance ocean observation, modelling, and decision-making.

Overview

The rapid expansion of ocean data, both in volume and complexity, offers transformative opportunities for ocean monitoring, forecasting, and decision-making. At the same time, it introduces significant challenges in ensuring data quality, interoperability, and trust.

To address these developments, EuroGOOS has established an Ad-hoc Group on Artificial Intelligence (AI). This group will provide a coordinated, community-driven approach to exploring how AI and machine learning can be responsibly and effectively integrated into operational oceanography.

Why this group matters

Across the EuroGOOS network, interest in AI is growing, from working groups and regional systems (ROOS) to member institutions. Discussions at recent events, including the General Assembly in Helsinki, highlighted key priorities:

  • Safeguarding data quality: AI must be built on validated datasets, with strong human oversight to ensure scientific integrity.
  • Learning from other domains: Fields like meteorology demonstrate the importance of consistent data standards and long-term data sharing practices.
  • Unlocking new opportunities: Emerging data types, such as biological observations and imaging, open new avenues for AI applications.
  • Maintaining user trust: Transparency in AI-generated outputs is essential to ensure reliability and confidence among users.

This ad-hoc group will help EuroGOOS take a structured and strategic approach.

Objectives

The group will focus on three main areas:

  • Strategic coordination and alignment
    Mapping existing AI initiatives across EuroGOOS and developing recommendations for future engagement, including the potential creation of a permanent AI Working Group.
  • Data quality and ethical AI use
    Promoting best practices that ensure transparency, accountability, and human-centric design in AI applications.
  • Knowledge sharing and capacity building
    Identifying challenges such as skills gaps and data readiness, while fostering collaboration through training, workshops, and shared resources.

List of members

Co-chairs

Jian Su

Danish Meteorological Institute

Denmark

Simone Spada

National Institute of Oceanography and Experimental Geophysics (OGS)

Italy

Members

EuroGOOS facilitation

Manuel Sala Pérez
Manuel Sala Pérez

Policy Officer