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

2026-06-16

AI Summary

Illegal, unreported, and unregulated (IUU) fishing and its associated supply chain crimes lack systematic quantitative understanding, hindering insights into their frequency, spatial distribution, actors, and behavioral patterns. This study introduces, for the first time, a large language model–driven framework to construct the IUU+ Global Event Database (IUU+DB), a comprehensive repository encompassing both environmental and labor-related violations. By integrating information extraction, named entity recognition, text classification, and deduplication algorithms, the system automatically derives structured data—including involved entities, locations, species, vessels, violation types, and enforcement outcomes—from heterogeneous textual sources. The resulting database effectively synthesizes fragmented evidence, identifies geographic and behavioral hotspots, and provides multidimensional data support for academic research, corporate risk assessment, and targeted governmental enforcement.

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.

Problem

• IUU fishing
• seafood fraud
• labor abuse
• illegal fishing
• supply chain crimes

Innovation

• IUU+DB
• large language model
• information extraction
• illegal fishing tracking
• structured incident database