Summary of Relevant Irrelevance: Generating Alterfactual Explanations For Image Classifiers, by Silvan Mertes et al.
Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers
by Silvan Mertes, Tobias Huber, Christina Karle, Katharina Weitz, Ruben Schlagowski, Cristina Conati, Elisabeth André
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 concept of alterfactual explanations for black box image classifiers, building upon traditional explanation mechanisms from the field of Counterfactual Thinking. The authors propose a novel approach to explain AI systems, dubbed alterfactual explanations, which involve showing an alternative reality where irrelevant features are altered. This allows users to directly see how input data characteristics can change without affecting the AI’s decision. To demonstrate this concept, the paper presents a GAN-based approach for generating alterfactual explanations for binary image classifiers and conducts a user study to evaluate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about explaining how artificial intelligence (AI) makes decisions. Right now, most AIs are like “black boxes” – we don’t know exactly how they make their choices. To fix this, researchers have been working on ways to explain AI’s decisions in a way that humans can understand. One idea is called “alterfactual explanations”. It involves showing what would happen if certain parts of the input data were changed. This helps people see which parts of the data really matter for the AI’s decision. |
Keywords
» Artificial intelligence » Gan
