Summary of Voice: Variance Of Induced Contrastive Explanations to Quantify Uncertainty in Neural Network Interpretability, by Mohit Prabhushankar and Ghassan Alregib
VOICE: Variance of Induced Contrastive Explanations to quantify Uncertainty in Neural Network Interpretability
by Mohit Prabhushankar, Ghassan AlRegib
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 paper proposes a method to visualize and quantify the predictive uncertainty of gradient-based post hoc visual explanations for neural networks. The authors show that existing evaluation strategies for explainability techniques partially reduce the predictive uncertainty of neural networks, allowing them to construct a plug-in approach to visualize and quantify the remaining uncertainty. The proposed method yields two key observations: firstly, explanatory techniques are often uncertain about the same features they attribute predictions to, reducing trustworthiness; secondly, objective metrics of an explanation’s uncertainty empirically behave similarly to epistemic uncertainty. The authors support these findings on two datasets, four explainability techniques, and six neural network architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making neural networks more transparent and trustworthy by figuring out how uncertain their predictions are. Right now, we don’t really know why they make the decisions they do, but this research helps us understand that uncertainty. It’s like trying to solve a puzzle – you have to consider what could go wrong before you can be sure of your answer. This study shows that when an AI system is unsure about something, it might point out features in an image even if those features aren’t really important for making the prediction. So, we need to be careful when relying on these systems to understand why they’re making certain decisions. |
Keywords
» Artificial intelligence » Neural network
