Summary of A Taxonomy Of Challenges to Curating Fair Datasets, by Dora Zhao et al.
A Taxonomy of Challenges to Curating Fair Datasets
by Dora Zhao, Morgan Klaus Scheuerman, Pooja Chitre, Jerone T.A. Andrews, Georgia Panagiotidou, Shawn Walker, Kathleen H. Pine, Alice Xiang
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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 This paper investigates the practical aspects of creating fairer machine learning (ML) datasets, focusing on the challenges and trade-offs encountered during the dataset curation lifecycle. By interviewing 30 ML dataset curators, the authors develop a comprehensive taxonomy of the obstacles faced. The findings highlight broader fairness issues that affect data curation, emphasizing the need for systemic changes to promote fair practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how people create datasets for machine learning and what problems they face. The researchers talked to 30 experts who make these datasets and found common challenges. They want to help make things better by showing ways to improve dataset creation so that it’s more fair. |
Keywords
» Artificial intelligence » Machine learning
