Explanation Transparency

Explanation Transparency is a conceptual framework within robot explainability that specifies the degree to which the rationale underlying a robotic system's decision-making process is comprehensible, accessible, and interpretable to human users.

33

Publications

8.2K

Citations

139

Authors

51

Institutions

Publications per year

2017–2026

31

Authors

139

  1. ML
    Michael Lewis

    University of Pittsburgh, United States

    3 publications · 168 citations

    3

    h-index

  2. KS
    Katia Sycara

    Carnegie Mellon University, United States

    3 publications · 168 citations

    3

    h-index

  3. JL
    Joy Lu

    Carnegie Mellon University, United States

    2 publications · 42 citations

    2

    h-index

  4. TW
    Tae Wan Kim

    Incheon National University, South Korea

    2 publications · 42 citations

    2

    h-index

  5. RS
    Ramitha Sundar

    University of Pittsburgh, United States

    2 publications · 151 citations

    2

    h-index

1–5 of 139

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 199 citations

    4

    h-index

  2. Machine Learning and Knowledge Extraction

    2 publications · 79 citations

    2

    h-index

  3. Journal of Cognitive Engineering and Decision Making

    JCEDM · 1 publications · 33 citations

    1

    h-index

  4. Human-Centric Intelligent Systems

    1 publications · 207 citations

    1

    h-index

  5. IEEE Transactions on Cognitive and Developmental Systems

    TCDS · 1 publications · 104 citations

    1

    h-index

1–5