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In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Longpre, Shayne, Klyman, Kevin, Appel, Ruth E., Kapoor, Sayash, Bommasani, Rishi, Sahar, Michelle, McGregor, Sean, Ghosh, Avijit, Blili-Hamelin, Borhane, Butters, Nathan, Nelson, Alondra, Elazari, Amit, Sellars, Andrew, Ellis, Casey John, Sherrets, Dane, Song, Dawn, Geiger, Harley, Cohen, Ilona, McIlvenny, Lauren, Srikumar, Madhulika, Jaycox, Mark M., Anderljung, Markus, Johnson, Nadine Farid, Carlini, Nicholas, Miailhe, Nicolas, Marda, Nik, Henderson, Peter, Portnoff, Rebecca S., Weiss, Rebecca, Westerhoff, Victoria, Jernite, Yacine, Chowdhury, Rumman, Liang, Percy, Narayanan, Arvind
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Accesso online

https://arxiv.org/abs/2503.16861