Summary of Sufficient and Necessary Explanations (and What Lies in Between), by Beepul Bharti et al.
Sufficient and Necessary Explanations (and What Lies in Between)
by Beepul Bharti, Paul Yi, Jeremias Sulam
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 formalizes two notions of feature importance for general machine learning models: sufficiency and necessity. Post-hoc explanation methods provide insights into which input features are crucial for a model’s output. However, these intuitive and simple explanations can be incomplete, missing important features that contribute to the model’s predictions. To address this limitation, the authors propose a unified notion of feature importance that explores a continuum along a necessity-sufficiency axis. This approach has strong ties to other popular definitions of feature importance, such as conditional independence and Shapley values. The paper demonstrates how the unified perspective can detect important features that might be overlooked by previous approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand why a machine learning model makes certain predictions. You want to know which parts of the input data are most important for those predictions. This paper introduces two ways to think about feature importance: sufficiency and necessity. While these ideas seem simple, they can be incomplete, leaving out important features that affect the model’s decisions. To fix this problem, the researchers create a new way to measure feature importance that combines the best of both approaches. This new method shows how it can find important features that other methods might miss. |
Keywords
* Artificial intelligence * Machine learning
