Summary of Navigating Dataset Documentations in Ai: a Large-scale Analysis Of Dataset Cards on Hugging Face, by Xinyu Yang et al.
Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face
by Xinyu Yang, Weixin Liang, James Zou
First submitted to arxiv on: 24 Jan 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 relies heavily on datasets, and documenting these datasets is crucial for ensuring their reliability, reproducibility, and transparency. This paper takes Hugging Face, a prominent platform for sharing ML models and datasets, as a case study to investigate current dataset documentation practices. Analyzing 7,433 dataset documents, the study reveals five key findings: (1) dataset popularity correlates with completion rates, (2) practitioners prioritize description and structure over considerations, (3) topic modeling highlights technical and social themes, including limitations, (4) improved accessibility and reproducibility are needed for usage sections, and (5) comprehensive content shapes perceptions of dataset quality. The study underscores the importance of thorough dataset documentation in machine learning research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is all about using computers to learn from data. This paper looks at how people document datasets on Hugging Face, a big platform where people share and work together on machine learning models and datasets. They analyzed over 7,000 dataset documents and found some interesting things: (1) not everyone does a good job of documenting their datasets, and it depends on how popular the dataset is, (2) most people focus on describing what’s in the dataset and how it’s structured, but don’t talk much about why you should use the data or what limitations there are, (3) when they do discuss limitations, it’s mostly about technical issues, not social ones, (4) datasets need to be more accessible and easy to reproduce for people to use them properly, and (5) if a dataset is well-documented, people will think it’s good. Overall, this study shows that documenting datasets is really important in machine learning. |
Keywords
* Artificial intelligence * Machine learning
