Machine Learning Source Attribution
Machine Learning Source Attribution is a scholarly contribution in the field of machine learning that identifies and characterizes the origin of data, models, or intellectual components within computational systems to establish provenance, accountability, and intellectual ownership in algorithmic development and deployment.
19
Publications
908
Citations
107
Authors
42
Institutions
Publications per year
2017–2026
19
Parents
19
A large-scale audit of dataset licensing and attribution in AI
Shayne Longpre, Robert Mahari, Anthony Chen et al. · Nature Machine Intelligence · 2024 · 63 citations · Full text
Restricting the Flow: Information Bottlenecks for Attribution
Karl Schulz, Leon Sixt, Federico Tombari et al. · arXiv (Cornell University) · 2020 · 55 citations · Full text
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107
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42
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
13 publications
549 citations
Altlabs
Berkeley, United States
2 h-index
8 publications
124 citations
University of Washington
Seattle, United States
2 h-index
4 publications
106 citations
Johns Hopkins Center for Health Security
Baltimore, United States
1 h-index
1 publications
13 citations
Duke University
Durham, United States
1 h-index
1 publications
33 citations
COMSATS University Islamabad
Islamabad, Pakistan
1 h-index
3 publications
36 citations

Shenzhen University
Shenzhen, China
1 h-index
2 publications
128 citations
North Dakota State University
Fargo, United States
1 h-index
4 publications
68 citations
1–8 of 42
Repository · 3 publications · 199 citations
3
h-index
2 publications · 46 citations
2
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1 publications · 64 citations
1
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1 publications · 12 citations
1
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1 publications · 26 citations
1
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