Summary of Exploring Commonalities in Explanation Frameworks: a Multi-domain Survey Analysis, by Eduard Barbu et al.
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis
by Eduard Barbu, Marharytha Domnich, Raul Vicente, Nikos Sakkas, André Morim
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 study explores the key elements for a universal explanation framework applicable across various domains, including medical, retail, and energy sectors. The research utilizes surveys and discussions with experts to identify essential components. A software tool, leveraging GP algorithms for interpretability, is developed based on these insights. The analyzed applications involve predictive machine learning in medical and energy scenarios, as well as prescriptive ML in retail. Experts from each sector shared their perspectives through interviews, while questionnaires were completed by professionals and non-experts to probe explanatory methods. Findings indicate a preference for sacrificing accuracy for greater explainability, emphasizing feature importance and counterfactual explanations as crucial components of the framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to figure out what makes it easy for people to understand how decisions were made in different fields like medicine, retail, and energy. They talked to experts and had them fill out questionnaires to see what works best. The researchers found that most people want explanations to be clear and simple, even if it means sacrificing some accuracy. They also learned that showing why certain features are important and providing alternative scenarios can help make decisions more transparent. |
Keywords
» Artificial intelligence » Machine learning
