Summary of Bayesnam: Leveraging Inconsistency For Reliable Explanations, by Hoki Kim et al.
BayesNAM: Leveraging Inconsistency for Reliable Explanations
by Hoki Kim, Jinseong Park, Yujin Choi, Seungyun Lee, Jaewook Lee
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 Neural Additive Model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that uses neural network-based architectures to provide intuitive explanations for its predictions. Despite their high performance, NAMs often produce inconsistent explanations even when using the same architecture and dataset. Instead of viewing these inconsistencies as issues to be resolved, researchers argue that they can provide valuable explanations within the given data model. A novel framework, Bayesian Neural Additive Model (BayesNAM), is introduced which integrates Bayesian neural networks and feature dropout, demonstrating that feature dropout effectively captures model inconsistencies. BayesNAM reveals potential problems such as insufficient data or structural limitations of the model, providing more reliable explanations and potential remedies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence can be really good at making predictions, but it’s not always clear why it makes those predictions. A new way to explain AI’s predictions is called the Neural Additive Model (NAM). NAMs use special kinds of computer networks to provide simple and understandable explanations for its predictions. However, researchers found that these explanations can be inconsistent even when using the same data and method. Instead of trying to fix this problem, scientists realized that these inconsistencies could actually provide valuable information about the data or the model itself. They developed a new way called Bayesian Neural Additive Model (BayesNAM) that uses a combination of techniques to capture these inconsistencies and reveal potential problems. |
Keywords
» Artificial intelligence » Dropout » Neural network
