ABSTRACT

Today, personnel selection practitioners in the United States are primarily guided by two streams of knowledge: 1) the development on the legal front pertaining to employment opportunities, and 2) the accumulation of findings in social, behavioral, and economic sciences that guide the accepted professional practices in personnel selection. The recent literature on fairness in machine learning offers a third stream of knowledge that practitioners can readily tap into when designing their personnel selection systems, yet a lack of integration between the machine learning literature and the two conventional streams of knowledge leaves a considerable gap preventing their effective integration. This research project focuses on bridging the gap to establish machine learning as the third pillar for the design of personnel selection systems in human resource management. The outcomes of the project inform policy makers and technology developers the factors important to the fairness of personnel selection. It also facilitates discussions about the use of machine learning in human resource management, by better connecting the empirical research of personnel selection with the technical design of fair machine learning algorithms.

The research in the project is rooted in the substantive bodies of multidisciplinary knowledge it integrates to enable fair personnel selection in the current legal structure. Specifically, the project develops a theoretical framework demonstrating how different design characteristics of a personnel selection system, from predictor selection to staging designs, influence and shape the Pareto front (in terms of tradeoff between selection validity and fairness) achievable under the prevailing employment opportunity laws. The findings from the theoretical framework speak to the importance of alignment between the design characteristics of a personnel selection system and the machine learning algorithms used within. Consequently, a key component of the project is a series of research tasks that combine theory development, algorithmic design, system implementation, and empirical research to properly situate the machine learning techniques within the current legal and industrial environments for personnel selection.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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