Explanation Completeness

Explanation Completeness is a theoretical construct in robot explainability that denotes the extent to which an explanation fully accounts for all relevant aspects of a robot’s decision-making process, behavior, and underlying model mechanics, thereby distinguishing it from partial or superficial explanation frameworks within the domain of explainable artificial intelligence.

20

Publications

1.3K

Citations

50

Authors

38

Institutions

Publications per year

2017–2026

10

Authors

50

  1. BK
    Been Kim

    Google (United States), United States

    2 publications · 70 citations

    2

    h-index

  2. PR
    Pradeep Ravikumar

    The University of Texas at Austin, United States

    2 publications · 70 citations

    2

    h-index

  3. SÖ
    Sercan Ö. Arık

    Stanford University, United States

    2 publications · 70 citations

    2

    h-index

  4. CY
    Chih‐Kuan Yeh

    Carnegie Mellon University, United States

    2 publications · 70 citations

    2

    h-index

  5. CL
    Chunliang Li

    St. Jude Children's Research Hospital, United States

    2 publications · 70 citations

    2

    h-index

1–5 of 50

Venues

  1. Artificial Intelligence

    4 publications · 193 citations

    4

    h-index

  2. Philosophy of Science

    2 publications · 123 citations

    2

    h-index

  3. Machine Learning

    2 publications · 71 citations

    2

    h-index

  4. SSRN Electronic Journal

    Repository · 1 publications · 30 citations

    1

    h-index

  5. Systematic Zoology

    1 publications · 19 citations

    1

    h-index

1–5