Zelin, Alexandra I.
Cunningham, Christopher J. L.; O'Leary, Brian J.
College of Arts and Sciences
University of Tennessee at Chattanooga
Place of Publication
Machine-learning artificial intelligence algorithms provide organizations with the opportunity to quickly and efficiently process information about potential employees while reducing costs associated with selection and turnover. However, any bias or error present in the programming of such algorithms as a result of information drawn from historically biased data is evident in the resulting output (Illingworth, 2015). Recently, applicants have expressed growing fairness and equity concerns about the risks associated with the use of algorithms in selection processes. The present quasi-experiment analyzed applicant reactions to selection processes to understand whether machine learning algorithms or human hiring decision-makers influence perceptions of fairness and equity and ultimately organizational attraction and job pursuit intentions. Applicants perceived more fairness and equity in the selection procedure when human evaluators reviewed applicant resumes compared to algorithmic evaluators. Additionally, the more fairness and equity applicants perceived, the stronger organizational attractiveness and the higher job pursuit intentions they reported.
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Attitude (Psychology); Employee selection; Machine learning
Industrial and Organizational Psychology
xii, 59 leaves
Warrenbrand, Megan, "Applicant justice perceptions of machine learning algorithms in personnel selection" (2021). Masters Theses and Doctoral Dissertations.