Explainable Data Analysis

Explainable Data Analysis is a systematic analytic approach within human-centered data analysis that enables transparent interpretation of data-driven insights by explicitly revealing the underlying rationale, assumptions, and mechanisms governing analytical outcomes.

65

Publications

5.1K

Citations

193

Authors

108

Institutions

Publications per year

2017–2026

44

Authors

193

  1. CS
    Christina Spolsky

    University of Illinois Urbana-Champaign, United States

    2 publications · 36 citations

    2

    h-index

  2. SM
    Sebastián Maldonado

    Universidad de Los Andes, Chile, Chile

    2 publications · 146 citations

    2

    h-index

  3. CA
    Cesare Alippi

    Politecnico di Milano, Italy

    2 publications · 213 citations

    2

    h-index

  4. BY
    Bin Yu

    Beihang University, China

    2 publications · 187 citations

    2

    h-index

  5. TU
    Thomas Uzzell

    University of Zurich, Switzerland

    2 publications · 36 citations

    2

    h-index

1–5 of 193

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 40 citations

    2

    h-index

  2. Annals of the New York Academy of Sciences

    2 publications · 36 citations

    2

    h-index

  3. IEEE Transactions on Fuzzy Systems

    TFS · 1 publications · 25 citations

    1

    h-index

  4. Information Sciences

    1 publications · 34 citations

    1

    h-index

  5. Applied Soft Computing

    1 publications · 23 citations

    1

    h-index

1–5