IUU+DB: Tracking Illegal, Unreported, and Unregulated Fishing, Seafood Fraud, and Labor Abuse through LLM-driven Information Extraction

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arXiv cs.AI · Henry Bodwell, Hong Yang, John C. Simeone, Kelvin Gorospe, Bella Sullivan, Lana Huang, Jessica Gephart, Sandy Aylesworth, Molly Masterton, Naren Ramakrishnan · 2026-07-08 AI

[Submitted on 16 Jun 2026 (v1), last revised 6 Jul 2026 (this version, v2)]

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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 type of illicit activity, remains difficult to obtain. We propose IUU+DB, a large language model driven system for building a global incident database of IUU+ activity. The system ingests heterogeneous documents, classifies whether they describe relevant incidents, extracts key data elements such as actors, locations, species, vessels, violations, and enforcement outcomes, and supports deduplication and trend analysis. Case studies and validation results show that IUU+DB can help organize fragmented evidence, surface geographic and behavioral hotspots, support fisheries-domain specific research in academia and non-government organizations, assist source and species risk assessments for industry, and provide support for policy implementation and targeted enforcement efforts to government agencies.

Submission history

From: Naren Ramakrishnan [view email]
[v1] Tue, 16 Jun 2026 17:16:05 UTC (1,643 KB)
[v2] Mon, 6 Jul 2026 19:48:46 UTC (1,642 KB)

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추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2606.18181

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