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

2025-05-10

AI Summary

Wild salmon populations in Indigenous rivers along North America’s Pacific Coast face mounting threats from climate change, habitat degradation, and limited data infrastructure in remote regions, impeding effective ecological monitoring. Method: This study introduces an AI–expert collaborative closed-loop management framework tailored to Indigenous watersheds. It integrates multimodal foundation models (video and sonar), active learning, culturally grounded expert-in-the-loop validation, and edge-deployable lightweight inference. Contribution/Results: The framework establishes the first co-modeling paradigm that jointly prioritizes ecological relevance and Indigenous cultural sovereignty. It achieves >92% species identification accuracy while reducing manual analysis time by 80%. Deployed across three Indigenous communities, it enables real-time fisheries decision-making and advances open-data governance and co-developed ethical AI practices grounded in Indigenous knowledge systems and data stewardship principles.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multimodal Learning

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Enhancing wild salmon monitoring in Indigenous rivers using AI
• Reducing manual effort in fisheries data collection and analysis
• Addressing technical and societal challenges in sustainable fisheries management

Innovation

• Multimodal AI for automated salmon species identification
• Expert-in-the-loop frameworks for ecological validation
• Interdisciplinary collaboration for ethical AI co-development