Public Sector Machine Learning

Public Sector Machine Learning is a scholarly contribution in the form of a method, model, framework, theory, construct, dataset, task, or metric that specifically advances the application of machine learning techniques to governance, public administration, or government services within the academic domain of Artificial Intelligence in Government.

84

Publications

8.5K

Citations

213

Authors

125

Institutions

Publications per year

2017–2026

83

Authors

213

  1. MV
    Michael Veale

    University College London, United Kingdom

    4 publications · 660 citations

    4

    h-index

  2. GM
    Gianluca Misuraca

    Joint Research Center, Spain

    3 publications · 947 citations

    3

    h-index

  3. CC
    Cary Coglianese

    University of Pennsylvania, United States

    2 publications · 246 citations

    2

    h-index

  4. MV
    Max Van Kleek

    University of Oxford, United Kingdom

    2 publications · 492 citations

    2

    h-index

  5. DL
    David Leslie

    The Alan Turing Institute, United Kingdom

    2 publications · 227 citations

    2

    h-index

1–5 of 213

Venues

  1. SSRN Electronic Journal

    Repository · 6 publications · 488 citations

    6

    h-index

  2. Government Information Quarterly

    5 publications · 1.5K citations

    5

    h-index

  3. Public Policy and Administration

    3 publications · 232 citations

    3

    h-index

  4. Nonprofit and Voluntary Sector Quarterly

    NVSQ · 2 publications · 54 citations

    2

    h-index

  5. International Journal of Public Administration

    IJPA · 2 publications · 1.2K citations

    2

    h-index

1–5