# On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning

arXiv: https://arxiv.org/abs/2401.10304 · HTML: https://mlsanity.com/paper/2401.10304/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [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)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards](https://mlsanity.com/paper/2108.07374/) (2021)
- [Risk of Training Diagnostic Algorithms on Data with Demographic Bias](https://mlsanity.com/paper/2005.10050/) (2020)
- [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022)
- [Towards Standardization of Data Licenses: The Montreal Data License](https://mlsanity.com/paper/1903.12262/) (2019)
- [A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication](https://mlsanity.com/paper/2111.04424/) (2021)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Theory and Practice of Data Citation](https://mlsanity.com/paper/1706.07976/) (2017)

## Abstract (arXiv)

To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, academic institutions' adoption of these practices has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers. In this study, we analyze how this broader scientific data documentation meets the needs of the ML community and regulatory bodies for its use in ML technologies. We examine a sample of 4041 data papers of different domains, assessing their completeness, coverage of the requested dimensions, and trends in recent years. We focus on the most and least documented dimensions and compare the results with those of an ML-focused venue (NeurIPS D&B track) publishing papers describing datasets. As a result, we propose a set of recommendation guidelines for data creators and scientific data publishers to increase their data's preparedness for its transparent and fairer use in ML technologies.