Summary of Building Better Datasets: Seven Recommendations For Responsible Design From Dataset Creators, by Will Orr and Kate Crawford
Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators
by Will Orr, Kate Crawford
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This study investigates the perspectives of leading dataset creators in machine learning, highlighting challenges and considerations they face. The authors conducted qualitative interviews with 18 experts, revealing seven key recommendations for responsible dataset creation. These include ensuring data quality, proper documentation, protecting privacy and consent, and mitigating potential harms from unintended use cases. By sharing these insights, the study aims to promote responsible practices and foster a deeper understanding of this crucial aspect of machine learning research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how people who create datasets for machine learning are important, but often overlooked. The authors asked 18 experts what they think about creating datasets responsibly. They found out that dataset creators face many challenges and have some great ideas on how to make sure datasets are good quality, well-documented, and safe. The study wants people to take these ideas seriously and start making better datasets. |
Keywords
» Artificial intelligence » Machine learning
