# Building Legal Datasets

arXiv: https://arxiv.org/abs/2111.02034 · HTML: https://mlsanity.com/paper/2111.02034/

## 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 (most cited first, 8 of 8)

- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://mlsanity.com/paper/2004.06100/) (2020)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [A Kernel Theory of Modern Data Augmentation](https://mlsanity.com/paper/1803.06084/) (2018)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP](https://mlsanity.com/paper/2109.06598/) (2021)
- [Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level](https://mlsanity.com/paper/2105.06020/) (2021)
- [A Comprehensive Analysis of Information Leakage in Deep Transfer Learning](https://mlsanity.com/paper/2009.01989/) (2020)

## Built on by (most cited first, 2 of 2)

- [The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI](https://mlsanity.com/paper/2310.16787/) (2023)
- [An Senegalese Legal Texts Structuration Using LLM-augmented Knowledge Graph](https://mlsanity.com/paper/2510.02353/) (2025)

## 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.