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Responsible AI metadata requirements for the Evaluations and Datasets Track NeurIPS 2026

This year, to improve transparency and responsible use of datasets, we are introducing a new requirement for dataset submissions in the NeurIPS 2026 Evaluations and Datasets Track: all dataset submissions must now include Responsible AI (RAI) metadata as part of the dataset’s Croissant file.

Dataset submissions to the track are already required to include a Croissant file, a standardized, machine-readable metadata format for describing datasets and their structure. We extend this requirement to also include RAI metadata within that file.

RAI metadata helps ensure datasets are used appropriately in AI research. Providing standardized information on how a dataset was created, its limitations, and its intended use helps researchers understand when and how the data can be used, reducing the risk of misuse, biased results, or misleading conclusions. This requirement also advances a core goal of the track: making datasets more transparent, comparable, and reusable across the community.

To make this as smooth as possible, we collaborated with the Croissant RAI team to establish a minimal set of RAI fields describing key aspects such as dataset limitations, potential biases, intended use, and other responsible AI considerations. Authors must include the RAI information directly within the Croissant metadata wherever possible, or clearly link to relevant sections of the paper when needed.

To support authors, we provide two tools to streamline this process:

We recommend completing these steps as early as possible, and inform authors that dataset submissions missing RAI metadata from their Croissant file will be flagged during review.

For full details on the dataset hosting and submission requirements, please see the Call for Papers and the data hosting guidelines. 

Joaquin Vanschoren, Konstantina Palla, Jessica Schrouff, Alexandre Drouin, Lijun Wu

Evaluations and Datasets Track Chairs 2026

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