Applicant justice perceptions of machine learning algorithms in personnel selection

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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Warrenbrand, Megan

Contributors dc:contributor

• Zelin, Alexandra I.
• Cunningham, Christopher J. L.; O'Leary, Brian J.
• College of Arts and Sciences

• Attitude (Psychology)
• Employee selection
• Machine learning

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/691

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1872