UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Epistola, Jordan JSubject: search.filters.subject.Employment Law

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    Comparing the Validity & Fairness of Machine Learning to Regression in Personnel Selection
    (2022) Epistola, Jordan J; Hanges, Paul J; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In 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.