Summary of Semantic Prototypes: Enhancing Transparency Without Black Boxes, by Orfeas Menis-mastromichalakis et al.
Semantic Prototypes: Enhancing Transparency Without Black Boxes
by Orfeas Menis-Mastromichalakis, Giorgos Filandrianos, Jason Liartis, Edmund Dervakos, Giorgos Stamou
First submitted to arxiv on: 18 Jul 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 A novel framework for machine learning (ML) models that enhances explainability and interpretability is introduced in this paper. The approach utilizes semantic descriptions to define prototypes and provide clear explanations, addressing the shortcomings of conventional methods. By leveraging concept-based descriptions to cluster data on the semantic level, the method ensures that prototypes represent underlying properties intuitively while being straightforward to interpret. This simplifies the interpretative process, bridging the gap between complex data structures and human cognitive processes, thereby enhancing transparency and fostering trust. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting more complicated, which means we need ways to understand how they work better. One way to do this is by using prototypes that capture important characteristics of the data. But traditional methods for creating these prototypes can be hard to understand and might even lead to misinterpretations. This paper presents a new method that uses words or phrases (semantic descriptions) to define what each prototype represents, making it easier to understand how they work. This approach is tested on various datasets and outperforms existing methods in terms of how well humans can understand the results. |
Keywords
* Artificial intelligence * Machine learning
