Summary of Machine Learning and Theory Ladenness — a Phenomenological Account, by Alberto Termine et al.
Machine Learning and Theory Ladenness – A Phenomenological Account
by Alberto Termine, Emanuele Ratti, Alessandro Facchini
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 (ML) research has raised questions about theory ladenness in scientific fields. Some argue that ML-assisted science is similar to traditional methods, with theory playing an essential role. Others claim that ML models are independent of domain theories. This paper argues that both positions are oversimplified and provides analysis on the interplay between ML methods and domain theories. Our study reveals that while constructing ML models can be relatively independent, practical implementation and interpretation rely on fundamental theoretical assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping scientists do their jobs better, but some people think it’s changing how science works. Some say it’s not very different from traditional science, where scientists use ideas to make sense of the world. Others think ML is so powerful that it can work without those ideas. This paper says both are wrong and looks at how ML fits into scientific research. We found out that while making models can be easy-peasy, actually using them in a specific field still needs some important background knowledge. |
Keywords
» Artificial intelligence » Machine learning
