Summary of Deriva-ml: a Continuous Fairness Approach to Reproducible Machine Learning Models, by Zhiwei Li et al.
Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models
by Zhiwei Li, Carl Kesselman, Mike D’Arch, Michael Pazzani, Benjamin Yizing Xu
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
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 In this paper, researchers explore ways to improve the quality of data used in machine learning (ML) applications for eScience problems. They find that current approaches often produce incorrect or unreproducible results due to poor data management. To address this issue, they propose using FAIR principles (findable, accessible, interoperable, and reusable) to manage data throughout an ML-based investigation’s life cycle. The authors demonstrate the effectiveness of their approach through two use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how scientists can make better decisions with artificial intelligence (AI). Right now, AI can sometimes give bad results or be hard to understand because people aren’t managing their data well. To solve this problem, scientists are working on new ways to handle data that will help them get more accurate and reliable results from AI. The authors of this paper show how using certain principles for handling data can make a big difference in the quality of AI-based scientific research. |
Keywords
» Artificial intelligence » Machine learning
