Illustrative example of AI-based species recognition on a coral reef—even when the model appears certain, expert validation remains essential to ensure reliable biodiversity monitoring. Credit: Leibniz-Zentrum für Marine Tropenforschung (ZMT)

Every day, thousands of images and signals are collected at sea. Sonar, buoys, satellites, and cameras installed on ships generate enormous amounts of data. Artificial intelligence (AI) is already being used to interpret this information. For example, to detect the presence of dolphins in real time to prevent bycatch, to estimate biodiversity indicators, or to automatically identify species caught onboard fishing vessels and improve fisheries management models. But behind this technological transformation emerges a key question: can we fully trust what AI says when the health of the ocean is at stake?

A European research team led by AZTI—Marine and Food Research in Spain and involving the Leibniz Centre for Tropical Marine Research (ZMT) in Germany has developed a framework that establishes three pillars for marine AI to be reliable, ethical, and scientifically robust. Although AI use is growing rapidly worldwide, global governance in the marine domain remains fragmented, with differing regulatory approaches across regions. The full work is detailed in the scientific article "Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management" and was published in Fish and Fisheries.

"We are seeing a massive increase in the use of AI algorithms that process the vast streams of marine data from cameras and sonar to satellite observations but they often fail to meet expectations," explains José A. Fernandes, AZTI AI expert and lead author of the study. "The key question is: how much trust can we place in these algorithms? Given that AI is already a reality for the fishing and marine research sector, it will only be useful if it is trustworthy. Our work establishes how to ensure trustworthiness by combining science, ethics, and industry engagement."

AI offers enormous possibilities but also risks. In the fisheries sector, an onboard camera system is used for automated catch monitoring. Without expert training data and images captured under diverse lighting conditions, it may confuse visually similar species. A model that predicts fish abundance can fail if it is built on incomplete or biased data, giving a misleading picture of the real state of a population.

Automated tools may also face resistance within the industry if their decision-making processes aren’t transparent or fail to reflect the practical knowledge of those who work at sea. These examples illustrate why robust criteria for quality, transparency, and validation are essential, especially in a field where decisions affect ecosystems, fishing communities, and public policy.

The framework proposed by the research team is structured around three main pillars. The first focuses on socioeconomic and legal viability. The development and use of AI must be accessible to all actors in the marine sector, including small-scale and artisanal fisheries, and aligned with international and regional regulations, such as the EU’s new AI Regulation, to ensure global coherence and fairness in implementation. The study emphasizes that the most effective tools are those designed with the direct participation of stakeholders, and not solely for them, which increases social acceptance, incorporates local knowledge, and reduces resistance.

The second pillar concerns the ethical governance of data. For AI to function effectively, it needs diverse, clean, traceable, and responsibly managed datasets. The authors recommend applying FAIR, CARE, and TRUST principles to marine data. These principles ensure that data are findable and accessible (Findable, Accessible, Interoperable, Reusable—FAIR), used responsibly (Collective Benefit, Authority to Control, Responsibility, Ethics—CARE), and managed in a trustworthy manner (Transparency, Responsibility, User focus, Sustainability, Technology—TRUST). This ensures that information such as images, sensor signals, or monitoring records remains compatible and respectful of the communities generating it, and preserved for long-term use.

Good data governance, the authors argue, is the foundation for transparency, reproducibility, and accountability across the entire data lifecycle.

"When AI is used to guide decisions that affect marine ecosystems and livelihoods, accessibility, transparency and validation are essential," says co-author Catarina Silva, researcher at the University of Coimbra, Portugal. "Our framework provides practical guidance to ensure that AI strengthens scientific evidence and trust across the marine sector."

The third pillar addresses technical robustness and scientific validation. AI must demonstrate its reliability under real-world ocean conditions not just in controlled environments. The researchers recommend validating models with independent data, applying statistical tests, and comparing outcomes with on-site measurements. For instance, automated catch analyses can be checked against manual port sampling to identify discrepancies. Such cross-validation ensures that algorithms reflect reality and deliver genuinely useful management tools.

Benefits for research, fishing, and society

The framework’s implications extend to the scientific community, administrations, the fishing sector, and the public.

For marine research, this guidance can provide coherent criteria for developing and benchmarking AI models, improving comparability, and accelerating insights into ecosystem health and climate impacts. For fisheries and environmental management, it can strengthen the reliability of decision-support systems—from quota allocation and marine spatial planning to the monitoring of illegal fishing. Properly validated models and well-governed data can help optimize routes, reduce emissions, enhance traceability.

Together, these advances support more sustainable operations at sea. For society, trustworthy AI ensures that ocean digitalization proceeds responsibly. It supports a sustainable blue economy, balancing technological innovation with social and ecological well-being, the authors argue. As AI becomes increasingly integrated into environmental governance, the researchers stress that regulation and ethics must evolve alongside technology.

"Regulating AI will be one of the defining governance challenges of our lifetime," says Julian Lilkendey, fisheries biologist at the Leibniz Centre for Tropical Marine Research (ZMT) in Germany and senior author of the study.

"In the ocean, where data and decisions shape both ecosystems and societies, AI must serve as a bridge between human judgment and machine precision. Only by aligning ethical governance, scientific validation, and social inclusion can we ensure that AI reinforces—not replaces—our capacity to make informed decisions about the sea."

Publication details

Jose A. Fernandes‐Salvador et al, Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management, Fish and Fisheries (2026). DOI: 10.1111/faf.70052

Provided by Leibniz-Zentrum für Marine Tropenforschung

Citation: Study offers practical guide for AI application in marine conservation and fisheries (2026, January 29) retrieved 29 January 2026 from https://phys.org/news/2026-01-ai-application-marine-fisheries.html

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