Summary of Practical Attribution Guidance For Rashomon Sets, by Sichao Li et al.
Practical Attribution Guidance for Rashomon Sets
by Sichao Li, Amanda S. Barnard, Quanling Deng
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed paper tackles the challenge of conflicting interpretations in Explainable AI (XAI) tasks, where different prediction models can perform equally well but offer distinct conclusions. The Rashomon effect is recognized as a critical factor, and the authors aim to address this issue by introducing practical guidelines for sampling methods. Specifically, they identify two fundamental axioms – generalizability and implementation sparsity – that these methods should satisfy in real-world applications. Notably, most existing attribution methods fail to meet these axioms, which highlights their fundamental weakness. To overcome this limitation, the authors design an -subgradient-based sampling method guided by these norms. They demonstrate the effectiveness of this approach on a mathematical problem and several practical datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making sense of why different AI models can give the same answer but mean different things. When we try to understand how AI makes decisions, we often get conflicting results. The authors want to solve this problem by introducing new rules for choosing which AI models to use and how to interpret their answers. They show that most current methods don’t follow these rules, so they develop a new way of sampling data that does. This helps us better understand why AI makes certain decisions and what it means for our lives. |
