Summary of Explainability in Ai Based Applications: a Framework For Comparing Different Techniques, by Arne Grobrugge et al.
Explainability in AI Based Applications: A Framework for Comparing Different Techniques
by Arne Grobrugge, Nidhi Mishra, Johannes Jakubik, Gerhard Satzger
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed method assesses the agreement of different explainability techniques by proposing a novel approach for evaluating the output of various methods. The authors compare six leading explainability techniques on top of the Vision Transformer model, which is widely used in business applications. This study aims to facilitate the integration of interpretable AI systems in practical business scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) has improved decision-making in industries like finance and healthcare. However, it’s hard for humans to understand how these AI systems make decisions because they are often “black boxes.” To address this issue, many explainability techniques have been developed. But, when choosing an explanation method, businesses need to balance accuracy with comprehensibility. This study solves the problem by proposing a new way to compare different explanation methods and choose the best one for practical use. |
Keywords
» Artificial intelligence » Vision transformer
