Summary of Generative Example-based Explanations: Bridging the Gap Between Generative Modeling and Explainability, by Philipp Vaeth et al.
Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
by Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen, Magda Gregorova
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 Recently, researchers have developed methods using deep generative modeling to provide example-based explanations for decision-making algorithms dealing with high-dimensional input data. Despite promising results, there is a disconnect between these approaches and classical explainability literature focused on lower-dimensional data with meaningful features. This gap in conceptual understanding and communication leads to misunderstandings and misaligned goals. To bridge this gap, we propose a novel probabilistic framework for local example-based explanations that integrates the essential characteristics of classical local explanation criteria while being suitable for high-dimensional data modeled through deep generative models. Our goal is to enhance communication, promote rigor, transparency, and improve research progress by facilitating peer discussion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to understand how decision-making algorithms work when they deal with lots of information. They’ve been using special computer models to explain these decisions. However, there’s a problem – the people who usually study explanation aren’t talking about the same thing as the researchers working on high-dimensional data. This mismatch makes it hard for people to agree and understand each other. To fix this, we’re proposing a new way to explain how algorithms work that combines the best parts of different approaches. We want to help people communicate better, make sure research is clear and transparent, and improve the overall progress. |
