Summary of Directly Optimizing Explanations For Desired Properties, by Hiwot Belay Tadesse et al.
Directly Optimizing Explanations for Desired Properties
by Hiwot Belay Tadesse, Alihan Hüyük, Weiwei Pan, Finale Doshi-Velez
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 limitations of existing methods in generating explanations for black-box machine learning models. These methods often “encourage” desirable properties, but this approach does not consistently produce explanations that meet these desired criteria. Moreover, it lacks control over prioritizing different properties when they conflict with each other. To address this issue, the authors propose a direct optimization approach to generate explanations that directly target specific properties. This method produces more consistent and controllable explanations, enabling users to create tailored explanations for various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to explain mysterious machine learning models. Right now, people use methods that try to make explanations good in certain ways, but these methods don’t always work as expected. Sometimes they produce bad explanations or don’t let people control what’s important and what’s not. The researchers propose a new way to create explanations that directly focuses on making them good in the right ways. This new approach is better at producing good explanations and lets users choose which aspects are most important. |
Keywords
* Artificial intelligence * Machine learning * Optimization
