Summary of Mountaineer: Topology-driven Visual Analytics For Comparing Local Explanations, by Parikshit Solunke et al.
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations
by Parikshit Solunke, Vitoria Guardieiro, Joao Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Claudio Silva
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Graphics (cs.GR); Algebraic Topology (math.AT)
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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 This research paper proposes a novel approach to evaluating and comparing local explainability methods for black-box machine learning models. The authors recognize the growing need for transparency and accountability in critical applications where ML techniques are increasingly being used. Despite the popularity of various local explainability methods, the existing solutions struggle with high dimensionality, heterogeneous representations, varying scales, and stochastic nature. The paper suggests leveraging Topological Data Analysis (TDA) to transform attributions into uniform graph representations, providing a common ground for comparison across different explanation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how we can explain why machine learning models make certain predictions. Right now, many people are using special techniques called “local explainability methods” to do this. However, it’s hard to compare and evaluate these methods because they work with very different things like images, words, or numbers. The authors suggest a new way to deal with this problem by using something called Topological Data Analysis (TDA). This method helps us turn the explanations into a common language that we can use to compare different methods. |
Keywords
* Artificial intelligence * Machine learning
