# A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset

arXiv: https://arxiv.org/abs/2506.17185 · HTML: https://mlsanity.com/paper/2506.17185/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2025.

## Built on by (most cited first, 6 of 6)

- [Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models](https://mlsanity.com/paper/2510.03721/) (2025)
- [The German Commons - 154 Billion Tokens of Openly Licensed Text for German Language Models](https://mlsanity.com/paper/2510.13996/) (2025)
- [How Do Data Owners Say No? A Case Study of Data Consent Mechanisms in Web-Scraped Vision-Language AI Training Datasets](https://mlsanity.com/paper/2511.08637/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Privacy in Image Datasets: A Case Study on Pregnancy Ultrasounds](https://mlsanity.com/paper/2602.07149/) (2026)
- [Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms](https://mlsanity.com/paper/2605.05224/) (2026)

## Abstract (arXiv)

We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped machine learning datasets? In an empirical study of a popular training dataset, we find significant presence of personally identifiable information despite sanitization efforts. Our audit provides concrete evidence to support the concern that any large-scale web-scraped dataset may contain legally defined personal data. We use these findings of a real-world dataset to inform our legal analysis with respect to existing privacy and data protection laws. We surface various legal risks of current data curation practices that may propagate personal information to train downstream models. Based on our empirical and legal analyses, we argue for reorientation of current frameworks of "publicly available" information to meaningfully limit the development of AI built upon indiscriminate scraping of the internet.