Summary of Reliability and Interpretability in Science and Deep Learning, by Luigi Scorzato
Reliability and Interpretability in Science and Deep Learning
by Luigi Scorzato
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); History and Philosophy of Physics (physics.hist-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 This paper investigates the reliability of Machine Learning (ML) methods, particularly Deep Neural Network (DNN) models, in light of their increasing use across various domains. By integrating standard error analysis with an epistemological examination of model assumptions, the authors highlight the complexities and implications of DNN models’ differences from traditional scientific modelling. The study emphasizes the importance of understanding model assumptions, which are language-independent and influenced by epistemic complexity. This leads to a discussion on the limitations of estimating reliability solely through statistical analysis. The paper also explores the connection between epistemic complexity and interpretability in responsible AI, underscoring the significance of interpretability for assessing reliability. Notably, the authors briefly discuss Random Forest and Logistic Regression models in this context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how trustworthy Machine Learning (ML) is. It’s becoming super important to know if ML methods are reliable or not. The study says that we can’t just use standard ways of analyzing errors when it comes to Deep Neural Networks (DNNs). Instead, we need to think about what these models assume and why they’re different from traditional scientific approaches. This means understanding the complexities of DNNs and how they affect our ability to predict their reliability. The study also talks about how complex models can be hard to understand, which makes it harder to decide if they’re reliable. |
Keywords
* Artificial intelligence * Logistic regression * Machine learning * Neural network * Random forest
