Summary of On the Readiness Of Scientific Data For a Fair and Transparent Use in Machine Learning, by Joan Giner-miguelez et al.
On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning
by Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
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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 educators can expect this study to shed light on the fairness and trustworthiness of ML systems. The research analyzes 4041 data papers from various domains, assessing completeness, coverage, and trends over time. It highlights the most and least documented dimensions, comparing results with those from an ML-focused venue like NeurIPS D&B track. This comprehensive study proposes guidelines for data creators and publishers to increase transparency and fairness in ML technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how scientists share their data and technical notes. They studied over 4,000 papers from different fields to see what’s working well and what isn’t. They also compared this with a special section of an important conference called NeurIPS D&B track that focuses on machine learning datasets. The study found some areas where more work is needed and gave suggestions for how scientists can do better. |
Keywords
* Artificial intelligence * Machine learning
