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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
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