Summary of Guarantee Regions For Local Explanations, by Marton Havasi et al.
Guarantee Regions for Local Explanations
by Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to ensuring interpretability of machine learning models is proposed, addressing limitations in current methods like LIME. The algorithm uses anchor-based techniques to identify regions where local explanations are guaranteed to be correct, providing a trustable feature-aligned box that matches the predictive model’s prediction. This method outperforms existing baselines by producing larger guarantee regions that better cover the data manifold. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can explain their predictions, but current methods have limitations. A new way to ensure these explanations are trustworthy is proposed. It finds areas where the explanation is guaranteed to be correct and provides a box that matches the model’s prediction. This helps identify reliable explanations and avoid misleading ones. |
Keywords
* Artificial intelligence * Machine learning