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

FAI：利用机器学习解决人员选择中的结构性偏见

• 批准号: 2040807
• 负责人: Nan Zhang
• 金额: $ 62.45万
• 依托单位: American University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040807&HistoricalAwards=false
• 关键词: FAI; Using; Machine; Learning; Address

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.

如今，美国的人事选拔从业者主要受到两条知识流的指导：1）与就业机会相关的法律方面的发展；2）指导公认的社会、行为和经济科学发现的积累。人员选拔方面的专业实践。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轻松利用这些知识流，但机器学习文献与这两种传统知识流之间缺乏整合，留下了相当大的差距，阻碍了他们的研究。有效整合。该研究项目的重点是弥合差距，将机器学习建立为人力资源管理中人员选拔系统设计的第三个支柱。该项目的结果为政策制定者和技术开发人员提供了对人员选拔公平性重要的因素。它还通过更好地将人员选拔的实证研究与公平的机器学习算法的技术设计联系起来，促进关于机器学习在人力资源管理中的应用的讨论。该项目的研究植根于它整合的多学科知识的实质性主体在当前的法律结构中实现公平的人员选拔。具体来说，该项目开发了一个理论框架，展示了人员选拔系统的不同设计特征（从预测选择到阶段设计）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（在选择有效性和公平性之间的权衡） 。理论框架的研究结果说明了人员选拔系统的设计特征与其中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，将理论开发、算法设计、系统实现和实证研究结合起来，以将机器学习技术正确地定位在当前的法律和工业环境中以进行人员选拔。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Reducing subgroup differences in personnel selection through the application of machine learning

通过机器学习的应用减少人员选拔中的亚组差异

• DOI: 10.1111/peps.12593
• 发表时间: 2023-06
• 期刊: Personnel Psychology
• 影响因子: 5.5
• 作者: Zhang, Nan;Wang, Mo;Xu, Heng;Koenig, Nick;Hickman, Louis;Kuruzovich, Jason;Ng, Vincent;Arhin, Kofi;Wilson, Danielle;Song, Q. Chelsea;et al
• 通讯作者: et al