Algorithmic Accountability

Algorithmic Accountability is a governance framework that specifies the mechanisms, responsibilities, and verification processes through which algorithmic systems are held answerable for their societal impacts, distinguishing it from related governance concepts by its focus on transparent, auditable, and enforceable obligations in the design, deployment, and oversight of automated decision-making.

218

Publications

24.2K

Citations

526

Authors

231

Institutions

Publications per year

2017–2026

201

Authors

526

  1. ID
    Inioluwa Deborah Raji

    University of California, Berkeley, United States

    8 publications · 1.5K citations

    8

    h-index

  2. ME
    Margot E. Kaminski

    University of Colorado Denver, United States

    8 publications · 565 citations

    8

    h-index

  3. EM
    Emanuel Moss

    Data & Society Research Institute, United States

    6 publications · 382 citations

    6

    h-index

  4. JM
    Jacob Metcalf

    Data & Society Research Institute, United States

    6 publications · 308 citations

    6

    h-index

  5. JS
    Jatinder Singh

    University of Cambridge, United Kingdom

    5 publications · 239 citations

    5

    h-index

1–5 of 526

Venues

  1. SSRN Electronic Journal

    Repository · 25 publications · 1.1K citations

    18

    h-index

  2. Big Data & Society

    9 publications · 4K citations

    9

    h-index

  3. arXiv (Cornell University)

    Repository · 8 publications · 397 citations

    8

    h-index

  4. 2022 ACM Conference on Fairness, Accountability, and Transparency

    Conference · 7 publications · 489 citations

    7

    h-index

  5. AI & Society

    6 publications · 525 citations

    6

    h-index

1–5