AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism

• URL: http://arxiv.org/abs/2503.17401v5
• Date: Mon, 28 Apr 2025 11:18:31 GMT
• Title: AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism
• Authors: Torsten Tiltack,
• Abstract summary: AIJIM is a framework for integrating real-time AI into environmental journalism.<n>It was validated in a 2024 pilot on the island of Mallorca.<n>It achieved 85.4% detection accuracy and 89.7% agreement with expert annotations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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