IUU+DB: Tracking Illegal, Unreported, and Unregulated Fishing, Seafood Fraud, and Labor Abuse through LLM-driven Information Extraction
arXiv:2606.18181v1 Announce Type: cross Abstract: Illegal, unreported, and unregulated fishing (IUU) traditionally refers to fishing activities that violate applicable laws or occur in areas that lack applicable laws. We propose the term IUU+ to capture a broader suite of fisheries sector environmental and associated supply chain trade-related crimes and behaviors. Although IUU+ activity is widely recognized as a serious threat to marine ecosystems, markets, and livelihoods, a quantitative understanding of these incidents, e.g., their frequency, geography, species, actors, and patterns in the
The increasing sophistication of LLMs and the urgent need to combat complex, intertwined environmental and economic crimes like IUU+ fishing are converging to enable new tracking capabilities.
This development indicates a growing application of advanced AI for real-world enforcement and monitoring, allowing for more comprehensive identification and quantitative understanding of illicit activities impacting critical global resources.
The ability to leverage LLMs for extracting and correlating information across vast datasets will enable a more robust and proactive approach to tracking and combating marine environmental crimes and associated supply chain abuses.
- · Environmental monitoring agencies
- · Law enforcement
- · Legitimate seafood industry
- · Marine ecosystems
- · Illegal fishing operations
- · Seafood fraudsters
- · Exploitative labor actors
- · Criminal organizations
Improved detection and prosecution rates for environmental and human rights abuses in the fishing industry.
Increased transparency and accountability across global seafood supply chains, potentially leading to shifts in consumer behavior and market dynamics.
The methodology could be adapted to track other complex, illicit activities by leveraging AI-driven information extraction for broader economic and security applications.
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Read at arXiv cs.AI