Summary of Unifying Attribution-based Explanations Using Functional Decomposition, by Arne Gevaert et al.
Unifying Attribution-Based Explanations Using Functional Decomposition
by Arne Gevaert, Yvan Saeys
First submitted to arxiv on: 18 Dec 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 The proposed unifying framework of attribution-based explanation methods aims to rigorously study the similarities and differences between various explanation methods in machine learning. The framework introduces removal-based attribution methods (RBAMs) and shows that many existing methods can be viewed as RBAMs. A canonical additive decomposition (CAD) is also introduced, which provides a general construction for additively decomposing any function. The authors demonstrate that every valid additive decomposition is an instance of the CAD, and that any removal-based attribution method is associated with a specific CAD. This intrinsic connection allows for the definition of formal descriptions of specific behaviors of explanation methods, as well as sufficient conditions for adherence to these behaviors. The framework has implications for developing new, efficient approximations for existing explanation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the “black box problem” in machine learning by introducing a new way to understand how complex models work. The authors show that many different methods for explaining models are actually related and can be understood through a common framework. They introduce a special kind of math called “canonical additive decomposition” that helps us see how these explanation methods fit together. By using this framework, the authors can define what makes each method unique and develop new ways to make explanations more efficient. |
Keywords
» Artificial intelligence » Machine learning
