Summary of Prototype-based Methods in Explainable Ai and Emerging Opportunities in the Geosciences, by Anushka Narayanan et al.
Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
by Anushka Narayanan, Karianne J. Bergen
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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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 A novel series of developments in prototype-based eXplainable Artificial Intelligence (XAI) has been explored, with a focus on their potential for scientific learning tasks in the geosciences. The literature is organized into three themes: prototype development and visualization, types of prototypes, and their application to various learning tasks. This work highlights how authors have utilized prototype-based methods, made novel contributions, and addressed limitations or challenges that may arise when adapting these methods for geoscientific applications. Additionally, the differences between geoscientific datasets and standard XAI benchmarks are discussed, as well as how specific geoscientific applications can benefit from using or modifying existing prototype-based XAI techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prototype-based XAI is a new way to understand how AI makes predictions. It works by comparing new data with examples of what the AI has learned before. This helps us see why the AI made a certain prediction. In this paper, researchers look at how well this method can be used for learning in the geosciences. They show that it can work well and make sense, even when dealing with complex geological data. |
