Comparing the Validity & Fairness of Machine Learning to Regression in Personnel Selection

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dc.contributor.advisorHanges, Paul Jen_US
dc.contributor.authorEpistola, Jordan Jen_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.accessioned2022-06-22T05:32:27Z
dc.date.available2022-06-22T05:32:27Z
dc.date.issued2022en_US
dc.description.abstractIn the realm of personnel selection, several researchers have claimed that machine learning (ML) can generate predictions that can out-predict more conventional methods such as regression. However, high-profile misuses of ML in selection contexts have demonstrated that ML can also result in illegal discrimination and/or bias against minority groups when developed improperly. This dissertation examined the utility of ML in personnel selection by examining the validity and fairness of ML methods relative to regression. Studies One and Two predicted counterproductive work behavior in Hanges et al.’s (2021) sample of Military cadets/midshipmen, and Study Three predicted job performance ratings of employees in Patalano & Huebner’s (2021) human resources dataset. Results revealed equivalent validity of ML to regression across all three studies. However, fairness was enhanced when ML was developed in accordance with employment law. Implications for the use of ML in personnel selection, as well as relevant legal considerations, are presented in my dissertation. Further, methods for further enhancing the legal defensibility of ML in the selection are discussed.en_US
dc.identifierhttps://doi.org/10.13016/yb4c-hspw
dc.identifier.urihttp://hdl.handle.net/1903/28963
dc.language.isoenen_US
dc.subject.pqcontrolledPsychologyen_US
dc.subject.pqcontrolledQuantitative psychologyen_US
dc.subject.pquncontrolledAssessmenten_US
dc.subject.pquncontrolledEmployment Lawen_US
dc.subject.pquncontrolledFairnessen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledPersonnel Selectionen_US
dc.subject.pquncontrolledValidityen_US
dc.titleComparing the Validity & Fairness of Machine Learning to Regression in Personnel Selectionen_US
dc.typeDissertationen_US

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