EAGER: Perceptions of Fairness and Justice in AI Software for Talent Acquisition

招聘和雇用中的公平和正义感受到几个因素的影响。一些因素是决策过程在不同人员和时间的一致性、及时和信息丰富的反馈、面试问题的适当性以及就业前测试与工作要求的关系。这些因素共同影响招聘和雇用的决策，并且越来越多地在人工智能（AI）的帮助下做出。在该项目中，应用社会技术框架来探索人工智能支持的人才获取算法的公平性和公正性。调查人员将收集和分析人力资源人员、非洲裔美国人求职者和人工智能软件设计者的看法。研究结果将用于为人类和所使用的算法设计偏见识别和缓解程序和技术，这项探索性研究的智力价值是开发定性工具和指标，可用于衡量算法公平和正义的看法。该研究方法通过使用三管齐下的方法扩展了选拔系统感知公平性的程序规则理论，包括在IT行业中代表性不足的求职者，管理人才获取过程的人力资源专业人员，以及设计AI软件的IT专业人员，公平性是产品设计和开发的核心价值。使用场景的看法进行检查，以及求职者谁是受这些决定的实际经验。这项研究有助于评估算法的公平性，目前还没有深入了解历史上被边缘化的人群如何看待人工智能系统或受到人工智能系统的不利影响。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Perceived fairness and justice in job recruiting and hiring are influenced by several factors. Some factors are the consistency of the decision-making process across people and time, timely and informative feedback, propriety of the interview questions, and the extent to which pre-employment tests appear to relate to the job requirements. These factors come together to influence decisions about recruiting and hiring and are being made increasingly with the help of artificial intelligence (AI). In this project, a sociotechnical frame is applied to explore perceptions of fairness and justice of AI-supported talent acquisition algorithms. the investigator will elicit and analyze perceptions of human resources personnel, African American job seekers, and AI software designers. The outcomes will be used to inform the design of bias recognition and mitigation procedures and technologies for both humans and the algorithms being used.The intellectual merit of this exploratory study is the development of qualitative instruments and metrics that can be used to measure perceptions of algorithmic fairness and justice. The research approach extends a theory of procedural rules for perceived fairness of selection systems by using a three-pronged approach comprising job seekers who are under-represented in the IT industry, human resource professionals who manage the talent acquisition process, and IT professionals who design AI software with fairness as the core value in product design and development. Perceptions using scenarios are examined as well as the actual experiences of jobseekers who are affected by these decisions. This research contributes to an assessment of algorithmic fairness at a time when there is currently little insight into how historically marginalized populations might perceive or be adversely affected by AI systems.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.