Summary of Sanity Checks For Explanation Uncertainty, by Matias Valdenegro-toro and Mihir Mulye
Sanity Checks for Explanation Uncertainty
by Matias Valdenegro-Toro, Mihir Mulye
First submitted to arxiv on: 25 Mar 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 A novel approach is proposed in this paper to evaluate the uncertainty associated with machine learning model explanations. The combination of explanation methods with uncertainty estimation methods produces “explanation uncertainty,” which can be challenging to assess. To address this challenge, the authors introduce sanity checks for uncertainty explanation methods, comprising weight and data randomization tests. These tests allow for rapid evaluation of combinations of uncertainty and explanation methods. Experimental results on the CIFAR10 and California Housing datasets demonstrate the validity and effectiveness of these tests, with Ensembles consistently passing both tests using Guided Backpropagation, Integrated Gradients, and LIME explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in machine learning by making it easier to check if explanation methods are accurate. When we try to understand how a machine learning model works, we need to be sure that the explanations we get are correct. The authors introduce two new tests to help us do this: one test checks the importance of different features and the other test checks for random patterns in the data. They show that these tests work well using three different explanation methods on two different datasets. |
Keywords
* Artificial intelligence * Backpropagation * Machine learning
