Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers

Xu, Chi; Jin, Yili; Ma, Sami; Qian, Rongsheng; Fang, Hao; Liu, Jiangchuan; Liu, Xue; Ngai, Edith C.H.; Atlas, William I.; Connors, Katrina M.; Spoljaric, Mark A.

doi:10.24963/ijcai.2025/1106

Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers

Chi Xu, Yili Jin, Sami Ma, Rongsheng Qian, Hao Fang, Jiangchuan Liu, Xue Liu, Edith C.H. Ngai, William I. Atlas, Katrina M. Connors, Mark A. Spoljaric

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 9954-9962.

https://doi.org/10.24963/ijcai.2025/1106

Keywords

Multidisciplinary Topics and Applications: General
Computer Vision: General
Machine Learning: General

Abstract

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Chi Xu, Yili Jin, Sami Ma, Rongsheng Qian, Hao Fang, Jiangchuan Liu, Xue Liu, Edith C.H. Ngai, William I. Atlas, Katrina M. Connors, and Mark A. Spoljaric. 2025. Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9954–9962. https://doi.org/10.24963/ijcai.2025/1106

@inproceedings{ijcai2025p1106,
  title     = {Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers},
  author    = {Xu, Chi and Jin, Yili and Ma, Sami and Qian, Rongsheng and Fang, Hao and Liu, Jiangchuan and Liu, Xue and Ngai, Edith C.H. and Atlas, William I. and Connors, Katrina M. and Spoljaric, Mark A.},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {9954--9962},
  year      = {2025},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2025/1106},
  url       = {https://doi.org/10.24963/ijcai.2025/1106},
}

TY  - CPAPER
TI  - Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers
AU  - Xu, Chi
AU  - Jin, Yili
AU  - Ma, Sami
AU  - Qian, Rongsheng
AU  - Fang, Hao
AU  - Liu, Jiangchuan
AU  - Liu, Xue
AU  - Ngai, Edith C.H.
AU  - Atlas, William I.
AU  - Connors, Katrina M.
AU  - Spoljaric, Mark A.
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 9954
EP  - 9962
DO  - 10.24963/ijcai.2025/1106
UR  - https://doi.org/10.24963/ijcai.2025/1106
ER  -