CITATION — REFERENCE ENTRY
A large-scale audit of dataset licensing and attribution in AI — Nature Machine Intelligence
- Key
- longpre-2024-dataset-licensing-audit
- Authors
- Longpre, Shayne; Mahari, Robert; Chen, Anthony; Obeng-Marnu, Naana; Sileo, Damien; Brannon, William; et al.
- Issued
- 2024-8-30
- Type
- article-journal
- Container
- Nature Machine Intelligence
- Volume
- 6
- Issue
- 8
- Pages
- 975-987
Raw CSL JSON
{
"DOI": "10.1038/s42256-024-00878-8",
"URL": "https://www.nature.com/articles/s42256-024-00878-8",
"page": "975-987",
"type": "article-journal",
"issue": "8",
"title": "A large-scale audit of dataset licensing and attribution in AI",
"author": [
{
"given": "Shayne",
"family": "Longpre"
},
{
"given": "Robert",
"family": "Mahari"
},
{
"given": "Anthony",
"family": "Chen"
},
{
"given": "Naana",
"family": "Obeng-Marnu"
},
{
"given": "Damien",
"family": "Sileo"
},
{
"given": "William",
"family": "Brannon"
},
{
"literal": "et al."
}
],
"issued": {
"date-parts": [
[
2024,
8,
30
]
]
},
"volume": "6",
"language": "en",
"container-title": "Nature Machine Intelligence"
}
Claims
-
An audit tracing the lineage of more than 1,800 text datasets found licence omission rates above 70% and error rates above 50% on popular dataset hosting sites.
"To improve data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace more than 1,800 text datasets. We develop tools and standards to trace the lineage of these datasets, including their source, creators, licences and subsequent use. [...] We observe frequent miscategorization of licences on popular dataset hosting sites, with licence omission rates of more than 70% and error rates of more than 50%. This highlights a crisis in misattribution and informed use of popular datasets driving many recent breakthroughs."
Available in