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

arXiv: https://arxiv.org/abs/2503.17401 · HTML: https://mlsanity.com/paper/2503.17401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences](https://mlsanity.com/paper/2112.08453/) (2021)
- [The Impact and Opportunities of Generative AI in Fact-Checking](https://mlsanity.com/paper/2405.15985/) (2024)

## Abstract (arXiv)

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.