Artificial Intelligence In Selection

Artificial Intelligence in Selection is a scholarly contribution in the field of candidate assessment that employs computational models and machine learning techniques to automate, enhance, or transform decision-making processes in personnel selection, distinguished from traditional methods by its capacity to learn from data, identify complex patterns, and make predictive evaluations with minimal human intervention.

147

Publications

7.7K

Citations

475

Authors

265

Institutions

Publications per year

2017–2026

101

Authors

475

  1. MA
    Michael A. Campion

    Purdue University West Lafayette, United States

    3 publications · 203 citations

    3

    h-index

  2. ED
    Emily D. Campion

    Purdue University West Lafayette, United States

    3 publications · 203 citations

    3

    h-index

  3. PK
    Priyanka Kaushik

    Chandigarh University, India

    2 publications · 121 citations

    2

    h-index

  4. RM
    Rabeb Mizouni

    Khalifa University of Science and Technology, United Arab Emirates

    2 publications · 44 citations

    2

    h-index

  5. JC
    John Capman

    University of Wisconsin–Milwaukee, United States

    2 publications · 163 citations

    2

    h-index

1–5 of 475

Venues

  1. Expert Systems with Applications

    5 publications · 865 citations

    5

    h-index

  2. SSRN Electronic Journal

    Repository · 3 publications · 78 citations

    3

    h-index

  3. Personnel Psychology

    2 publications · 92 citations

    2

    h-index

  4. Journal of Business and Psychology

    2 publications · 132 citations

    2

    h-index

  5. Academic Medicine

    2 publications · 96 citations

    2

    h-index

1–5