Summary of Logic-based Explainability: Past, Present & Future, by Joao Marques-silva
Logic-Based Explainability: Past, Present & Future
by Joao Marques-Silva
First submitted to arxiv on: 4 Jun 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 This paper surveys recent advances in explainable AI (XAI), a crucial aspect of trustworthy AI. Logic-based XAI, a rigorous approach to XAI, has emerged as an alternative to non-rigorous methods. The paper provides an overview of the origins and current research topics in logic-based XAI, highlighting its potential for building trust in high-risk domains. Additionally, it debunks common myths surrounding non-rigorous approaches to XAI. Keywords: explainable AI, trustworthy AI, logic-based XAI, rigorous validation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making artificial intelligence (AI) more understandable and trustworthy. Right now, some AI systems are too complicated for humans to understand why they make certain decisions. This can be a problem because important decisions might not be thoroughly checked. The paper looks at a way to make AI more transparent called logic-based explainable AI. It explains how this method works, what it’s good for, and clears up misconceptions about other approaches. |
