Explanation Consistency

Explanation Consistency is a property of explainable machine learning models that refers to the degree to which similar inputs or model behaviors yield similar explanations, ensuring stability and reliability in interpretability across variations in data or model perturbations.

34

Publications

2.6K

Citations

111

Authors

56

Institutions

Publications per year

2017–2026

24

Authors

111

  1. HL
    Himabindu Lakkaraju

    Harvard University Press, United States

    4 publications · 271 citations

    4

    h-index

  2. PN
    P. N. Johnson‐Laird

    Princeton University, United States

    3 publications · 248 citations

    3

    h-index

  3. SJ
    Shahin Jabbari

    Harvard University Press, United States

    2 publications · 197 citations

    2

    h-index

  4. CO
    Chun Ouyang

    Queensland University of Technology, Australia

    2 publications · 279 citations

    2

    h-index

  5. SK
    Sangeet Khemlani

    United States Naval Research Laboratory, United States

    2 publications · 68 citations

    2

    h-index

1–5 of 111

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 94 citations

    4

    h-index

  2. 2

    h-index

  3. Machine Learning

    2 publications · 56 citations

    2

    h-index

  4. Information Sciences

    2 publications · 44 citations

    2

    h-index

  5. Journal of Cognitive Psychology

    1 publications · 30 citations

    1

    h-index

1–5