# Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation

arXiv: https://arxiv.org/abs/2503.13463 · HTML: https://mlsanity.com/paper/2503.13463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?](https://mlsanity.com/paper/2202.06675/) (2022)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development](https://mlsanity.com/paper/2108.04308/) (2021)
- [Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?](https://mlsanity.com/paper/1912.08320/) (2019)
- [Algorithmic Fairness Datasets: the Story so Far](https://mlsanity.com/paper/2202.01711/) (2022)
- [Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers](https://mlsanity.com/paper/2108.02922/) (2021)
- [Fairness in Ranking: A Survey](https://mlsanity.com/paper/2103.14000/) (2021)
- [A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication](https://mlsanity.com/paper/2111.04424/) (2021)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [Augmented Datasheets for Speech Datasets and Ethical Decision-Making](https://mlsanity.com/paper/2305.04672/) (2023)
- [A Methodology for Creating AI FactSheets](https://mlsanity.com/paper/2006.13796/) (2020)
- [A Nutritional Label for Rankings](https://mlsanity.com/paper/1804.07890/) (2018)

## Built on by (most cited first, 3 of 3)

- [Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond](https://mlsanity.com/paper/2507.08866/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation](https://mlsanity.com/paper/2604.09617/) (2026)

## Abstract (arXiv)

ML/AI is the field of computer science and computer engineering that arguably received the most attention and funding over the last decade. Data is the key element of ML/AI, so it is becoming increasingly important to ensure that users are fully aware of the quality of the datasets that they use, and of the process generating them, so that possible negative impacts on downstream effects can be tracked, analysed, and, where possible, mitigated. One of the tools that can be useful in this perspective is dataset documentation. The aim of this work is to investigate the state of dataset documentation practices, measuring the completeness of the documentation of several popular datasets in ML/AI repositories. We created a dataset documentation schema -- the Documentation Test Sheet (DTS) -- that identifies the information that should always be attached to a dataset (to ensure proper dataset choice and informed use), according to relevant studies in the literature. We verified 100 popular datasets from four different repositories with the DTS to investigate which information was present. Overall, we observed a lack of relevant documentation, especially about the context of data collection and data processing, highlighting a paucity of transparency.