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

@article{Fernandes‐Salvador2026,
  Title = {Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management},
  Author = {Fernandes‐Salvador, Jose A. and Borja, Angel and Anabitarte, Asier and Granado, Igor and Lekunberri, Xabier and Sagarminaga, Yolanda and Canals, Oriol and Lanzen, Anders and Azhar, Mihailo and Kotta, Jonne and Ojaveer, Henn and Spinosa, Anna and Jokinen, Ari‐Pekka and Haraguchi, Lumi and Stæhr, Sanjina Upadhyay and Pérez, Aritz and Inza, Iñaki and Villasante, Sebastian and Oanta, Gabriela A. and Silva, Catarina N. S. and Tiller, Rachel and Lilkendey, Julian},
  Editor = {},
  Journal = {Fish and Fisheries},
  Year = {2026},
  Pages = {248-263},
  Volume = {27},
  Doi = {10.1111/faf.70052},
  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.},
}

TY  - JOUR
AU  - Fernandes‐Salvador, Jose A.
AU  - Borja, Angel
AU  - Anabitarte, Asier
AU  - Granado, Igor
AU  - Lekunberri, Xabier
AU  - Sagarminaga, Yolanda
AU  - Canals, Oriol
AU  - Lanzen, Anders
AU  - Azhar, Mihailo
AU  - Kotta, Jonne
AU  - Ojaveer, Henn
AU  - Spinosa, Anna
AU  - Jokinen, Ari‐Pekka
AU  - Haraguchi, Lumi
AU  - Stæhr, Sanjina Upadhyay
AU  - Pérez, Aritz
AU  - Inza, Iñaki
AU  - Villasante, Sebastian
AU  - Oanta, Gabriela A.
AU  - Silva, Catarina N. S.
AU  - Tiller, Rachel
AU  - Lilkendey, Julian
TI  - Towards Trustworthy Artificial Intelligence for Marine Research, Fisheries and Environmental Management
T2  - Fish and Fisheries
PY  - 2026
SP  - 248-263
VL  - 27
DO  - 10.1111/faf.70052
AB  - 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.
ER  -