Global Explanations

Global Explanations is a type of model-agnostic interpretability method that characterizes the overall behavior of a machine learning model across its entire input domain, distinguishing itself from local explanations by providing comprehensive insights into global decision patterns rather than instance-specific predictions.

112

Publications

16.2K

Citations

416

Authors

178

Institutions

Publications per year

2017–2026

98

Authors

416

  1. HL
    Himabindu Lakkaraju

    Harvard University Press, United States

    4 publications · 184 citations

    4

    h-index

  2. FG
    Fosca Giannotti

    University of Pisa, Italy

    3 publications · 376 citations

    3

    h-index

  3. DP
    Dino Pedreschi

    University of Pisa, Italy

    3 publications · 376 citations

    3

    h-index

  4. AM
    Anna Monreale

    University of Pisa, Italy

    3 publications · 376 citations

    3

    h-index

  5. RC
    Rich Caruana

    Cornell University, United States

    3 publications · 231 citations

    3

    h-index

1–5 of 416

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 933 citations

    16

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 330 citations

    5

    h-index

  3. SSRN Electronic Journal

    Repository · 4 publications · 137 citations

    4

    h-index

  4. Artificial Intelligence

    3 publications · 263 citations

    3

    h-index

  5. IEEE Access

    3 publications · 476 citations

    3

    h-index

1–5