Summary of Rethinking Explainable Machine Learning As Applied Statistics, by Sebastian Bordt et al.
Rethinking Explainable Machine Learning as Applied Statistics
by Sebastian Bordt, Eric Raidl, Ulrike von Luxburg
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Machine learning explanations are a rapidly growing area, but there’s often confusion about what these algorithms are for and how to use them. A new paper argues that explanation models should borrow from traditional statistical methods, treating explanations as high-dimensional function statistics. This perspective highlights the importance of interpretation and understanding how explanations relate to human intuition. The lack of discussion around this topic is a major drawback in current research papers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is trying to explain why it makes certain decisions, but it’s not doing a great job. A new idea says we should think about explaining like traditional statistics. It’s all about understanding what’s going on and how it relates to the world around us. This is important because right now, researchers aren’t talking enough about this. |
Keywords
* Artificial intelligence * Machine learning
