Summary of Causal Feature Selection For Responsible Machine Learning, by Raha Moraffah et al.
Causal Feature Selection for Responsible Machine Learning
by Raha Moraffah, Paras Sheth, Saketh Vishnubhatla, Huan Liu
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Machine learning has become essential in many real-world applications, prompting the need for responsible machine learning that aligns with ethical and social values. The survey focuses on four key issues: interpretability, fairness, adversarial robustness, and domain generalization. It highlights the importance of feature selection in responsible ML tasks, but notes that relying on statistical correlations can lead to biased patterns and compromised performance. Causal feature selection is proposed as a unique approach to ensure ML models are ethical and socially responsible in high-stakes applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has become super important in many areas of life. Now, we need to make sure it’s working well and being fair. This big report looks at four main problems: making sense of results, being equal, staying strong against tricks, and working across different areas. It also talks about picking the right features for these tasks. But sometimes, using just statistics can lead to bad results. A new way to pick features is suggested to make sure machine learning works well in important situations. |
Keywords
* Artificial intelligence * Domain generalization * Feature selection * Machine learning * Prompting
