AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Tiltack, Torsten
Language: English
Topic: Journalism and Media Studies
Subfield: Communication
Field: Social Sciences
Domain: Social Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W6929428980
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