Summary of Applications Of Explainable Artificial Intelligence in Earth System Science, by Feini Huang et al.
Applications of Explainable artificial intelligence in Earth system science
by Feini Huang, Shijie Jiang, Lu Li, Yongkun Zhang, Ye Zhang, Ruqing Zhang, Qingliang Li, Danxi Li, Wei Shangguan, Yongjiu Dai
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper reviews explainable artificial intelligence (XAI) and its applications in Earth system science (ESS). The authors highlight the importance of XAI in making AI models more transparent and accountable. They begin by explaining the concept of XAI, typical methods, and then review XAI applications in ESS literature, showcasing its role in facilitating communication with AI model decisions, improving model diagnosis, and uncovering scientific insights. The authors identify four significant challenges that XAI faces within ESS and propose solutions. They also provide a comprehensive illustration of multifaceted perspectives. The paper concludes by suggesting an interpretable hybrid approach to enhance the utility of AI in ESS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper talks about using artificial intelligence (AI) to help us understand the Earth and its systems. One problem with AI is that it’s hard to figure out how it comes up with its answers, so we need ways to make it more transparent. The authors review a way called explainable AI (XAI) that helps us understand how AI models work. They look at how XAI has been used in studying the Earth and its systems, and they discuss some challenges and solutions for using XAI in this field. |
