FAI: Using Machine Learning to Address Structural Bias in Personnel Selection

今天，美国的人事选拔从业者主要由两个知识流指导：1)与就业机会有关的法律方面的发展，2)社会、行为和经济科学中指导公认的人事选拔专业实践的研究结果的积累。最近关于机器学习公平性的文献提供了第三种知识流，从业者在设计人员选拔系统时可以很容易地利用这些知识流，但机器学习文献和两种传统知识流之间缺乏整合，导致它们之间存在相当大的差距，阻碍了它们的有效整合。本研究项目致力于将机器学习作为人力资源管理人才选拔系统设计的第三支柱。该项目的结果为政策制定者和技术开发人员提供了对公平选拔人员非常重要的因素。它还促进了关于在人力资源管理中使用机器学习的讨论，将人事甄选的经验研究与公平机器学习算法的技术设计更好地结合在一起。该项目的研究植根于它整合的多学科知识的实体，以便在当前的法律结构中实现公平的人事甄选。具体地说，该项目制定了一个理论框架，说明人员甄选制度的不同设计特点--从预测者甄选到阶段性设计--如何影响和塑造在现行就业机会法下可实现的帕累托前沿(在甄选有效性和公平性方面的权衡)。从理论框架中得出的结论说明了人员甄选系统的设计特点与内部使用的机器学习算法之间保持一致的重要性。因此，该项目的一个关键组成部分是一系列研究任务，这些任务结合了理论开发、算法设计、系统实施和实证研究，以在当前的法律和行业环境中恰当地定位机器学习技术，用于人员选拔。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.