Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management

ABSTRACT Artificial Intelligence (AI) is advancing at an unprecedented pace, offering transformative opportunities for marine research, fisheries management, environmental governance and policy development. Particularly in the context of the interconnected data needs of ecosystem management and biodiversity conservation, these technologies can enhance data acquisition, processing and decision support, enabling more integrated approaches to ecosystem management and biodiversity conservation. Yet their adoption in these domains remains limited by the absence of coherent frameworks that ensure transparency, validation and ethical alignment with ecological and socio‐economic sustainability goals. This work proposes a comprehensive framework built on three critical pillars for trustworthy AI: socio‐economic and legal viability, data governance and technical and scientific robustness. On the one hand it aims to be a guideline for developer teams. On the other hand, it aims to be a guideline for final users (e.g., industry and managers) for designing the requirements and evaluating such systems. The first pillar underscores the need for AI systems that are cost‐effective, scalable, environmentally sustainable and legally supported, balancing short‐term costs with long‐term social and ecological benefits. The second stresses adherence to fair, reliable and ethical access to digital resources, recognising that without strong governance data and algorithms risk becoming fragmented or misused. The third pillar addresses the necessity of rigorous validation across entire AI pipelines, including preprocessing, model evaluation and benchmarking against alternative ground truths, to ensure reliability in real‐world applications. Together, these pillars provide a blueprint for developing ethical, reliable and policy‐relevant AI systems that can strengthen trust, improve sustainability and guide decision‐making across marine science, fisheries, environmental management and European legislation.

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Publication Details

Journal
Fish and Fisheries
Published
2026-01-21
DOI
https://doi.org/10.1111/faf.70052
Primary Topic
Coastal and Marine Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management

Anders Lanzén, Xabier Lekunberri, Lumi Haraguchi, Julian Lilkendey et al.
Fish and Fisheries
Coastal and Marine Management
article

Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management

Anders Lanzén, Xabier Lekunberri, Lumi Haraguchi, Julian Lilkendey, Oriol Canals, Sanjina Upadhyay Stæhr, Iñaki Inza, C. Silva, Mihailo Azhar, Yolanda Sagarmínaga, Igor Granado, Anna Spinosa, Ángel Borja, S. Villasante, Asier Anabitarte, Gabriela A. Oanta, Ari‐Pekka Jokinen, Henn Ojaveer, Jonne Kotta, Jose A. Fernandes‐Salvador, Aritz Pérez, Rachel Tiller
article en

Abstract

ABSTRACT Artificial Intelligence (AI) is advancing at an unprecedented pace, offering transformative opportunities for marine research, fisheries management, environmental governance and policy development. Particularly in the context of the interconnected data needs of ecosystem management and biodiversity conservation, these technologies can enhance data acquisition, processing and decision support, enabling more integrated approaches to ecosystem management and biodiversity conservation. Yet their adoption in these domains remains limited by the absence of coherent frameworks that ensure transparency, validation and ethical alignment with ecological and socio‐economic sustainability goals. This work proposes a comprehensive framework built on three critical pillars for trustworthy AI: socio‐economic and legal viability, data governance and technical and scientific robustness. On the one hand it aims to be a guideline for developer teams. On the other hand, it aims to be a guideline for final users (e.g., industry and managers) for designing the requirements and evaluating such systems. The first pillar underscores the need for AI systems that are cost‐effective, scalable, environmentally sustainable and legally supported, balancing short‐term costs with long‐term social and ecological benefits. The second stresses adherence to fair, reliable and ethical access to digital resources, recognising that without strong governance data and algorithms risk becoming fragmented or misused. The third pillar addresses the necessity of rigorous validation across entire AI pipelines, including preprocessing, model evaluation and benchmarking against alternative ground truths, to ensure reliability in real‐world applications. Together, these pillars provide a blueprint for developing ethical, reliable and policy‐relevant AI systems that can strengthen trust, improve sustainability and guide decision‐making across marine science, fisheries, environmental management and European legislation.

Fish and FisheriesVol. 27(2)
Universidade da Coruña (ES), Ikerbasque (ES), Institute of the Estonian Language (EE), University of the Basque Country (ES), SINTEF (NO), Universidade de Santiago de Compostela (ES), Aarhus University (DK), Leibniz Centre for Tropical Marine Research (DE), Finnish Environment Institute (FI), Basque Center for Applied Mathematics (ES), Auckland University of Technology (NZ), AZTI (ES), Deltares (NL), Southern District Health Board (NZ), Pärnu Museum (EE), University of Tartu (EE), University of Coimbra (PT), University of Otago (NZ)
Openalex Percentile: Top 86%
Coastal and Marine Management
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