{
 "id": "nvidia/Aegis-AI-Content-Safety-Dataset-2.0",
 "owner": "nvidia",
 "name": "Aegis-AI-Content-Safety-Dataset-2.0",
 "downloads": 16014,
 "likes": 116,
 "last_modified": "2025-06-09T19:15:56.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "@inproceedings{ghosh-etal-2025-aegis2,\n    title = \"{AEGIS}2.0: A Diverse {AI} Safety Dataset and Risks Taxonomy for Alignment of {LLM} Guardrails\",\n    author = \"Ghosh, Shaona and Varshney, Prasoon and Sreedhar, Makesh Narsimhan and Padmakumar, Aishwarya and Rebedea, Traian and Varghese, Jibin Rajan and Parisien, Christopher\",\n    editor = \"Chiruzzo, Luis and Ritter, Alan and Wang, Lu\",\n    booktitle = \"Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)\",\n    month = apr,\n    year = \"2025\",\n    address = \"Albuquerque, New Mexico\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.naacl-long.306/\",\n    doi = \"10.18653/v1/2025.naacl-long.306\",\n    pages = \"5992--6026\",\n    ISBN = \"979-8-89176-189-6\"\n}",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive."
}