Summary of The Multiple Dimensions Of Spuriousness in Machine Learning, by Samuel J. Bell and Skyler Wang
The Multiple Dimensions of Spuriousness in Machine Learning
by Samuel J. Bell, Skyler Wang
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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) and artificial intelligence (AI) research rely heavily on learning correlations from data. However, this approach can fail when unintended correlations are captured, leading to concerns about spuriousness. Spuriousness is often viewed as an impediment to model performance, fairness, and robustness. This paper explores how ML researchers conceptualize and address spuriousness in practice. By analyzing a broad survey of ML literature, the authors identify multiple dimensions of spuriousness, including relevance, generalizability, human-likeness, and harmfulness. These dimensions demonstrate that spuriousness goes beyond the causal/non-causal dichotomy and can influence the trajectory of ML development. The paper contributes to ongoing debates about responsible practices in AI development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers are trying to figure out how to make sure their models don’t pick up on useless patterns in data. This is important because it can affect how well a model works, whether it’s fair and trustworthy, and even if it could cause harm. The paper talks about different ways that researchers think about this problem, like making sure the patterns they find are relevant to what they’re trying to do, or that they generalize to new situations. It also looks at how people want models to work in a way that’s similar to how humans would do things. By understanding these different perspectives, the paper helps us think about how we can make artificial intelligence more responsible and beneficial. |
Keywords
* Artificial intelligence * Machine learning
