AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism
It addresses the problem of timely and participatory environmental reporting for journalists and communities, offering a transferable model that is incremental over existing approaches.
The paper tackles real-time AI integration in environmental journalism by introducing AIJIM, a scalable framework that achieved 85.4% detection accuracy and reduced reporting latency by 40% in a pilot study.
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.