AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism
Torsten Tiltack
TL;DR
AIJIM presents a scalable, modular framework for real-time environmental journalism that fuses Vision Transformer-based hazard detection, crowdsourced validation, and automated reporting with dual CAM-LIME explainability. Demonstrated in the 2024 Mallorca Mallorca pilot on the NamicGreen platform, it processed 1,000 citizen-submitted images, detected 50 undocumented waste sites with $85.4\%$ accuracy and $89.7\%$ expert agreement, and achieved a $40\%$ reduction in reporting latency. The model emphasizes participatory validation, ethical governance, and compatibility with SDGs and the EU AI Act, offering a transferable approach to AI-enhanced environmental reporting. Collectively, AIJIM advances rapid, transparent, and scalable journalism by integrating real-time AI, human-in-the-loop verification, and explainability to empower communities and policymakers.
Abstract
This paper introduces AIJIM, the Artificial Intelligence Journalism Integration Model -- a novel framework for integrating real-time AI into environmental journalism. AIJIM combines Vision Transformer-based hazard detection, crowdsourced validation with 252 validators, and automated reporting within a scalable, modular architecture. A dual-layer explainability approach ensures ethical transparency through fast CAM-based visual overlays and optional LIME-based box-level interpretations. Validated in a 2024 pilot on the island of Mallorca using the NamicGreen platform, AIJIM achieved 85.4\% detection accuracy and 89.7\% agreement with expert annotations, while reducing reporting latency by 40\%. Unlike conventional approaches such as Data-Driven Journalism or AI Fact-Checking, AIJIM provides a transferable model for participatory, community-driven environmental reporting, advancing journalism, artificial intelligence, and sustainability in alignment with the UN Sustainable Development Goals and the EU AI Act.