Building Legal Datasets
Not on the map. Why?
Abstract (arXiv)
Data-centric AI calls for better, not just bigger, datasets. As data protection laws with extra-territorial reach proliferate worldwide, ensuring datasets are legal is an increasingly crucial yet overlooked component of ``better''. To help dataset builders become more willing and able to navigate this complex legal space, this paper reviews key legal obligations surrounding ML datasets, examines the practical impact of data laws on ML pipelines, and offers a framework for building legal datasets.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 3 Nov 2021.
Builds on
8 of 37 references.
References · 8
- 2020Pretrained Transformers Improve Out-of-Distribution Robustnesscited by 294
- 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 260
- 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
- 2018A Kernel Theory of Modern Data Augmentationcited by 106
- 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
- 2021Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLPcited by 47
- 2021Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Levelcited by 34
- 2020A Comprehensive Analysis of Information Leakage in Deep Transfer Learningcited by 4
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.