Perceived Algorithmic Risk

Perceived Algorithmic Risk is a psychological construct denoting the subjective assessment of potential harm, unfairness, or adverse consequences associated with the deployment and decision-making of algorithmic systems, distinguished from objective risk by its dependence on individual beliefs, experiences, and contextual cues in the evaluation of algorithmic behavior.

31

Publications

3.8K

Citations

83

Authors

60

Institutions

Publications per year

2017–2026

28

Authors

83

  1. JB
    Jürgen Buder

    Leibniz-Institut für Wissensmedien, Germany

    2 publications · 145 citations

    2

    h-index

  2. IB
    Irina Brich

    Leibniz-Institut für Wissensmedien, Germany

    2 publications · 145 citations

    2

    h-index

  3. NS
    Nadia Said

    University of Tübingen, Germany

    2 publications · 145 citations

    2

    h-index

  4. MH
    Markus Huff

    University of Tübingen, Germany

    2 publications · 145 citations

    2

    h-index

  5. HS
    Hanna Schumm

    Leibniz-Institut für Wissensmedien, Germany

    1 publications · 77 citations

    1

    h-index

1–5 of 83

Venues

  1. Computers in Human Behavior

    5 publications · 257 citations

    5

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  2. AI & Society

    2 publications · 91 citations

    2

    h-index

  3. Electronic Commerce Research

    1 publications · 39 citations

    1

    h-index

  4. Annals of Operations Research

    1 publications · 41 citations

    1

    h-index

  5. 1

    h-index

1–5