Fernandes‐Salvador, Jose A., Borja, Angel, Anabitarte, Asier, Granado, Igor, Lekunberri, Xabier, Sagarminaga, Yolanda, Canals, Oriol, Lanzen, Anders, Azhar, Mihailo, Kotta, Jonne, Ojaveer, Henn, Spinosa, Anna, Jokinen, Ari‐Pekka, Haraguchi, Lumi, Stæhr, Sanjina Upadhyay, Pérez, Aritz, Inza, Iñaki, Villasante, Sebastian, Oanta, Gabriela A., Silva, Catarina N. S., Tiller, Rachel and Lilkendey, Julian ORCID: https://orcid.org/0000-0003-3165-1079 (2026) Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management. Fish and Fisheries . DOI https://doi.org/10.1111/faf.70052.

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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.

Document Type:	Article
Programme Area:	PA1, PA5
Research affiliation:	Science Management > Office for Knowledge Exchange
Refereed:	Yes
Open Access Journal?:	No
DOI:	https://doi.org/10.1111/faf.70052
ISSN:	1467-2960
Date Deposited:	30 Jan 2026 18:11
Last Modified:	30 Jan 2026 18:11
URI:	https://cris.leibniz-zmt.de/id/eprint/6084

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