The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI

2024 article longpre2024dataprovenance ○ Not yet verified

Authors

Shayne Longpre, Robert Mahari, Anthony Chen, Naana Obeng-Marnu, Damien Sileo, William Brannon, Niklas Muennighoff, Nathan Khazam, Jad Kabbara, Kartik Perisetla, Xinyi Wu, Enrico Shippole, Kurt Bollacker, Tongshuang Wu, Luis Villa, Sandy Pentland, Sara Hooker

Venue

Nature Machine Intelligence

Abstract

Large-scale audit of over 1,800 text AI datasets analyzing trends, permissions of use and global representation. Found frequent miscategorization of licences on dataset hosting sites, with licence omission rates of more than 70% and error rates of more than 50%. Released the Data Provenance Explorer tool for practitioners.

BibTeX

Local Entry

@article{longpre2024dataprovenance,
  title = {The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI},
  author = {Shayne Longpre and Robert Mahari and Anthony Chen and Naana Obeng-Marnu and Damien Sileo and William Brannon and Niklas Muennighoff and Nathan Khazam and Jad Kabbara and Kartik Perisetla and Xinyi Wu and Enrico Shippole and Kurt Bollacker and Tongshuang Wu and Luis Villa and Sandy Pentland and Sara Hooker},
  year = {2024},
  journal = {Nature Machine Intelligence},
  url = {https://www.nature.com/articles/s42256-024-00878-8},
  abstract = {Large-scale audit of over 1,800 text AI datasets analyzing trends, permissions of use and global representation. Found frequent miscategorization of licences on dataset hosting sites, with licence omission rates of more than 70% and error rates of more than 50%. Released the Data Provenance Explorer tool for practitioners.}
}

External Source

Not found in external databases.