Algorithmic Fairness

Algorithmic Fairness is a normative framework within data ethics that specifies criteria for the equitable treatment of individuals or groups by automated decision-making systems, distinguishing itself from related ethical principles through its focus on mitigating biases and ensuring justice in algorithmic outcomes.

2.6K

Publications

216.3K

Citations

6.6K

Authors

1.8K

Institutions

Publications per year

2017–2026

2.3K

Authors

6.6K

  1. KP
    Krishna P. Gummadi

    Max Planck Institute for Software Systems, Germany

    27 publications · 3.3K citations

    21

    h-index

  2. AW
    Adrian Weller

    University of Cambridge, United Kingdom

    25 publications · 1.9K citations

    20

    h-index

  3. AR
    Aaron Roth

    University of Pennsylvania, United States

    22 publications · 3K citations

    19

    h-index

  4. MK
    Michael Kearns

    University of Pennsylvania, United States

    20 publications · 2.4K citations

    18

    h-index

  5. KR
    Kush R. Varshney

    IBM Research - Thomas J. Watson Research Center, United States

    16 publications · 2.2K citations

    14

    h-index

1–5 of 6.6K

Venues

  1. arXiv (Cornell University)

    Repository · 340 publications · 16.8K citations

    57

    h-index

  2. AI and Ethics

    44 publications · 3K citations

    31

    h-index

  3. SSRN Electronic Journal

    Repository · 84 publications · 6.3K citations

    29

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 59 publications · 2.6K citations

    27

    h-index

  5. 2022 ACM Conference on Fairness, Accountability, and Transparency

    Conference · 31 publications · 1.8K citations

    25

    h-index

1–5