  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.
