Summary of Quce: the Minimisation and Quantification Of Path-based Uncertainty For Generative Counterfactual Explanations, by Jamie Duell et al.
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
by Jamie Duell, Monika Seisenberger, Hsuan Fu, Xiuyi Fan
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Quantified Uncertainty Counterfactual Explanations (QUCE), a novel approach to improve interpretability in Deep Neural Networks (DNNs). As DNNs scale to complex tasks, their decisions become less transparent. AGI, a type of explainable model, leverages path-based gradients from DNNs to elucidate their decisions. However, irregularities during out-of-distribution path traversal compromise the performance of these models. QUCE mitigates this issue by minimizing path uncertainty and generating more certain counterfactual examples. The paper compares QUCE with competing methods for both path-based explanations and generative counterfactual examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer that can make predictions, but it’s hard to understand why it made those predictions. This is a problem called “explainability.” A new way to solve this problem is called QUCE (Quantified Uncertainty Counterfactual Explanations). It helps us understand how the computer reached its conclusions and also gives us more reliable examples of what might have happened if things had gone differently. The researchers compared their new method with other approaches and showed that it works better. |
