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

Authors

107

  1. JS
    Jacob Swett

    Massachusetts Institute of Technology, United States

    2 publications · 46 citations

    2

    h-index

  2. EC
    Ethan C. Alley

    Massachusetts Institute of Technology, United States

    2 publications · 46 citations

    2

    h-index

  3. KM
    Kevin M. Esvelt

    Massachusetts Institute of Technology, United States

    2 publications · 46 citations

    2

    h-index

  4. MZ
    Muhammad Zubair Malik

    North Dakota State University, United States

    1 publications · 17 citations

    1

    h-index

  5. DS
    Damien Sileo

    Harvard University Press, United States

    1 publications · 63 citations

    1

    h-index

1–5 of 107

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 199 citations

    3

    h-index

  2. Nature Communications

    2 publications · 46 citations

    2

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. Symmetry

    1 publications · 26 citations

    1

    h-index

1–5