A Standardized Machine-readable Dataset Documentation Format for Responsible AI

TL;DR

This paper introduces Croissant-RAI, a standardized, machine-readable dataset documentation format to improve discoverability, interoperability, and trustworthiness of AI datasets, supporting responsible AI practices.

Contribution

It extends existing metadata standards to create a community-driven, adaptable format integrated with web practices and tools for better dataset documentation in AI.

Findings

Croissant-RAI is adopted by major data platforms.

It improves dataset discoverability and trustworthiness.

Supports community-driven, evolving documentation standards.

Abstract

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.