AIJIM让环保新闻实时生成，靠视觉模型+众包验证
AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism
- 用视觉变换器检测环境灾害，结合众包验证提升准确性
- 在马略卡岛测试中达85.4%检测准确率，报告延迟降低40%
- 适合关注可持续发展与透明AI的媒体机构和研究者
本文提出AIJIM——人工智能新闻融合模型，一种将实时AI融入环境新闻报道的新框架。该模型结合基于视觉变换器的灾害检测、252名志愿者参与的众包验证，以及可扩展的模块化架构实现自动化报道。采用双层可解释性设计，通过快速CAM可视化叠加和可选的LIME盒级解释保障伦理透明度。在2024年利用NamicGreen平台于马略卡岛开展的试点中，AIJIM实现85.4%的检测准确率和89.7%与专家标注的一致性，同时将报告延迟降低40%。相比传统数据驱动新闻或AI事实核查，AIJIM为参与式、社区驱动的环境报道提供可迁移范式，推动新闻业、人工智能与可持续发展目标及欧盟人工智能法案的协同发展。
原文摘要 · Abstract (English)
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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