Explainable Artificial Intelligence

Explainable Artificial Intelligence is a subfield of artificial intelligence that comprises methodologies and frameworks designed to render the decisions and internal workings of machine learning models transparent, interpretable, and comprehensible to human users, thereby distinguishing it from opaque or black-box predictive modeling approaches.

2.4K

Publications

250.1K

Citations

8.3K

Authors

2.5K

Institutions

Publications per year

2017–2026

2.4K

Authors

8.3K

  1. AH
    Andreas Holzinger

    Medical University of Graz, Austria

    25 publications · 4.1K citations

    22

    h-index

  2. KM
    Klaus‐Robert Müller

    Technische Universität Berlin, Germany

    19 publications · 4.8K citations

    18

    h-index

  3. WS
    Wojciech Samek

    Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Germany

    19 publications · 5.4K citations

    17

    h-index

  4. QV
    Q. Vera Liao

    Microsoft (Canada), Canada

    17 publications · 1.7K citations

    16

    h-index

  5. BK
    Been Kim

    Google (United States), United States

    15 publications · 5.1K citations

    14

    h-index

1–5 of 8.3K

Venues

  1. arXiv (Cornell University)

    Repository · 273 publications · 26.5K citations

    65

    h-index

  2. IEEE Access

    82 publications · 13.7K citations

    47

    h-index

  3. Applied Sciences

    44 publications · 1.9K citations

    30

    h-index

  4. Scientific Reports

    45 publications · 2.5K citations

    29

    h-index

  5. Sensors

    26 publications · 1.5K citations

    21

    h-index

1–5