Summary of Explainable Artificial Intelligence and Multicollinearity : a Mini Review Of Current Approaches, by Ahmed M Salih
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approaches
by Ahmed M Salih
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 paper reviews state-of-the-art approaches for Explainable Artificial Intelligence (XAI) to address the multicollinearity issue when generating explanations of machine learning models. XAI aims to understand how AI systems make decisions by identifying the most informative features. The study highlights the current limitations of XAI methods in dealing with multicollinearity and suggests future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews recent advances in Explainable Artificial Intelligence (XAI) that help us understand how machine learning models make decisions. It focuses on how XAI handles a big problem called multicollinearity, which can make it hard to understand AI systems. The study looks at seven papers and discusses their strengths and weaknesses. |
Keywords
* Artificial intelligence * Machine learning
