# A Standardized Machine-readable Dataset Documentation Format for Responsible AI

arXiv: https://arxiv.org/abs/2407.16883 · HTML: https://mlsanity.com/paper/2407.16883/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [DICES Dataset: Diversity in Conversational AI Evaluation for Safety](https://mlsanity.com/paper/2306.11247/) (2023)
- [Introducing v0.5 of the AI Safety Benchmark from MLCommons](https://mlsanity.com/paper/2404.12241/) (2024)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)

## Built on by (most cited first, 7 of 7)

- [The Impact of Modern AI in Metadata Management](https://mlsanity.com/paper/2501.16605/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation](https://mlsanity.com/paper/2505.17841/) (2025)
- [SIEVE: Towards Verifiable Certification for Code-datasets](https://mlsanity.com/paper/2510.02166/) (2025)
- [A Policy Profile for Croissant: Refusal as a Property of the Dataset](https://mlsanity.com/paper/2609.19640/) (2026)
- [A Matched-Budget Audit Framework for Recaptioned Image-Text Supervision Distributions](https://mlsanity.com/paper/2610.00952/) (2026)
- [CroissantMiner: Automated Extraction and Validation of Croissant Metadata for ML Datasets](https://mlsanity.com/paper/2610.07132/) (2026)

## Abstract (arXiv)

Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croissant-RAI, a machine-readable metadata format designed to enhance the discoverability, interoperability, and trustworthiness of AI datasets. Croissant-RAI extends the Croissant metadata format and builds upon existing responsible AI (RAI) documentation frameworks, offering a standardized set of attributes and practices to facilitate community-wide adoption. Leveraging established web-publishing practices, such as Schema.org, Croissant-RAI enables dataset users to easily find and utilize RAI metadata regardless of the platform on which the datasets are published. Furthermore, it is seamlessly integrated into major data search engines, repositories, and machine learning frameworks, streamlining the reading and writing of responsible AI metadata within practitioners' existing workflows. Croissant-RAI was developed through a community-led effort. It has been designed to be adaptable to evolving documentation requirements and is supported by a Python library and a visual editor.