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About this paper
Nitisha Jain, Mubashara Akhtar, Joan Giner-Miguelez, Rajat Shinde, Joaquin Vanschoren, Steffen Vogler, Sujata Goswami, Yuhan Rao and 10 more
Croissant-RAI extends the Croissant dataset-metadata format with machine-readable fields for responsible-AI documentation, covering provenance, collection, annotation, participation, bias, privacy, intended use, and maintenance. Implemented through JSON-LD, a Python library, and a web editor, the vocabulary is illustrated on geospatial, human-annotation, and multilingual text datasets, though its evaluation remains primarily qualitative and design-oriented.
Key insights
- Responsible-AI documentation can be integrated into existing dataset metadata ecosystems rather than maintained as a separate, disconnected documentation layer.
- The proposed scope treats responsibility as a life-cycle concern, linking collection, annotation, participation, fairness, privacy, safety, release, and maintenance information.
- Standardized fields improve interoperability and potential machine processing, but free-form entries, incomplete disclosure, and inconsistent interpretation still limit automation and assurance.
Summary by alphaXiv.
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