Human-centered Explainable Machine Learning

Human-Centered Explainable Machine Learning is an academic framework within explainable artificial intelligence that prioritizes human stakeholders' cognitive, contextual, and experiential needs by systematically integrating human-centric design principles, interpretability requirements, and interactive feedback loops into the development, evaluation, and deployment of machine learning models.

69

Publications

5.1K

Citations

349

Authors

181

Institutions

Publications per year

2017–2026

69

Authors

349

  1. DA
    Daniel A. Keim

    University of Konstanz, Germany

    2 publications · 240 citations

    2

    h-index

  2. TG
    Thanassis Giannetsos

    Technical University of Denmark, Denmark

    2 publications · 304 citations

    2

    h-index

  3. KK
    Kristian Kersting

    Technische Universität Darmstadt, Germany

    2 publications · 219 citations

    2

    h-index

  4. NB
    Nadia Bianchi‐Berthouze

    University College London, United Kingdom

    2 publications · 189 citations

    2

    h-index

  5. YM
    Yao Ming

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 123 citations

    2

    h-index

1–5 of 349

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 152 citations

    8

    h-index

  2. ACM Transactions on Interactive Intelligent Systems

    TIIS · 3 publications · 222 citations

    3

    h-index

  3. IEEE Access

    3 publications · 149 citations

    3

    h-index

  4. Information Fusion

    2 publications · 678 citations

    2

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 21 citations

    1

    h-index

1–5