Summary of Explaining Explainability: Recommendations For Effective Use Of Concept Activation Vectors, by Angus Nicolson et al.
Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors
by Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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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 paper explores the internal representations of deep learning models through concept-based explanations. Concept Activation Vectors (CAVs) are a popular method for identifying concepts, but their inconsistencies across layers, entanglement with other concepts, and spatial dependencies pose challenges in interpreting models. The authors introduce tools to detect these properties and provide recommendations to mitigate their impact on explanation quality. They demonstrate practical applications by applying their findings to a melanoma classification task, showing how entanglement can lead to uninterpretable results. Additionally, they create a new synthetic dataset, Elements, designed to capture known relationships between concepts and classes, releasing it for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how deep learning models work. It’s trying to figure out what the models are thinking when they make decisions. The authors look at something called Concept Activation Vectors (CAVs), which help us understand what the models know. They found that CAVs can be confusing because they’re not always consistent, and some concepts are mixed up with others. This can lead to bad explanations of how the model is working. To fix this, the authors created tools to detect these problems and gave recommendations for making better explanations. They even used their findings to improve a task where doctors try to diagnose skin cancer. |
Keywords
* Artificial intelligence * Classification * Deep learning
