Summary of Evolutionary Approaches to Explainable Machine Learning, by Ryan Zhou et al.
Evolutionary approaches to explainable machine learning
by Ryan Zhou, Ting Hu
First submitted to arxiv on: 23 Jun 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 chapter in the field of explainable artificial intelligence (XAI) or explainable machine learning (XML) explores the potential of evolutionary computing in providing transparency and accountability for machine learning models. By reviewing current techniques in XAI/XML and discussing how evolutionary computing can contribute to this field, the authors highlight the significance of using powerful optimization and learning tools to address concerns about model black-box nature. The chapter also touches on open challenges and opportunities for future research in XAI/XML, emphasizing the importance of developing more transparent, trustworthy, and accountable machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is becoming super smart, but we don’t always know how it’s making decisions. This can be a problem because AI is being used in important areas like healthcare and finance. To fix this, researchers are working on “explainable” AI that can show us why it made certain choices. One way to do this is by using something called evolutionary computing. In this chapter, experts share how they’re using evolutionary computing to make AI more transparent and trustworthy. They also talk about what’s missing in the field and where we need more work. |
Keywords
* Artificial intelligence * Machine learning * Optimization
