APPLICANT REACTIONS TO ARTIFICIAL INTELLIGENCE SELECTION SYSTEMS

dc.contributor.advisorWessel, Jenniferen_US
dc.contributor.authorBedemariam, Rewina Sahleen_US
dc.contributor.departmentPsychologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-02-01T06:30:43Z
dc.date.available2023-02-01T06:30:43Z
dc.date.issued2022en_US
dc.description.abstractPractitioners have embraced the use of AI and Machine Learning systems for employeerecruitment and selection. However, studies examining applicant reactions to such systems are lacking in the literature. Specifically, little is known about how job applicants react to AI-based selection systems. This study assessed fairness perceptions of hiring decisions made by AIdriven systems and whether significant differences existed between different groups of people.  To do so, a two-by-two experimental study where participants in a selection scenario are randomly assigned to a decision-maker condition (human vs AI) and outcome variability condition (hired vs rejected) was utilized. The results showed that the condition had a significant effect on the interactional justice dimension. The interaction effect of outcome and condition had an impact on job-relatedness, chance to perform, reconsideration opportunity, feedback perceptions, and interactional justice. The three-way interaction of outcome, race and condition influences general fairness reactions and emotional reactions. Given these findings, HR personnel should weigh the pros and cons of AI, especially towards applicants that are rejected.en_US
dc.identifierhttps://doi.org/10.13016/quol-qzpo
dc.identifier.urihttp://hdl.handle.net/1903/29536
dc.language.isoenen_US
dc.subject.pqcontrolledOrganizational behavioren_US
dc.subject.pqcontrolledOccupational psychologyen_US
dc.subject.pquncontrolledAIen_US
dc.subject.pquncontrolledApplicant Reactionsen_US
dc.subject.pquncontrolledBiasen_US
dc.subject.pquncontrolledDiversityen_US
dc.subject.pquncontrolledOutcome Favorabilityen_US
dc.subject.pquncontrolledRaceen_US
dc.titleAPPLICANT REACTIONS TO ARTIFICIAL INTELLIGENCE SELECTION SYSTEMSen_US
dc.typeThesisen_US

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