Summary of Explainable Artificial Intelligence Techniques For Accurate Fault Detection and Diagnosis: a Review, by Ahmed Maged et al.
Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review
by Ahmed Maged, Salah Haridy, Herman Shen
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 investigates the application of Explainable Artificial Intelligence (XAI) tools and techniques in machine learning, particularly in the manufacturing industry where sensor integration and automation are becoming increasingly prevalent. The authors review various XAI methodologies that aim to make AI decision-making transparent, highlighting their role in critical scenarios involving human involvement. They also discuss current limitations and potential future research directions that balance explainability with model performance while improving trustworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making artificial intelligence more understandable. Right now, AI models are like black boxes – we don’t know how they make decisions. This is a problem when humans need to work with these machines. The authors explore ways to make AI more transparent, which can help us trust the decisions it makes. They look at different methods for doing this and discuss what’s missing and what could be improved. |
Keywords
» Artificial intelligence » Machine learning
