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

EAGER：对人工智能软件人才招聘公平正义的看法

• 批准号: 1841368
• 负责人: Lynette Yarger
• 金额: $ 22.51万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841368&HistoricalAwards=false
• 关键词: EAGER; Perceptions; Fairness; Justice; AI

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.

招聘和雇用过程中的公平性和公正性受到多种因素的影响。一些因素包括人员和时间上决策过程的一致性、及时且信息丰富的反馈、面试问题的适当性以及就业前测试与工作要求的相关程度。这些因素共同影响着招聘和聘用决策，并且越来越多地在人工智能 (AI) 的帮助下做出。在这个项目中，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正义感。调查员将引出并分析人力资源人员、非裔美国求职者和人工智能软件设计师的看法。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可用于衡量算法认知的定性工具和指标。公平正义。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，该方法包括在 IT 行业中代表性不足的求职者、管理人才招聘流程的人力资源专业人员以及设计人才的 IT 专业人员。以公平为核心价值的人工智能软件在产品设计和开发中。研究了使用情景的看法以及受这些决定影响的求职者的实际经历。这项研究有助于对算法公平性进行评估，目前人们对历史上边缘化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知之甚少。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。

项目成果

Algorithmic equity in the hiring of underrepresented IT job candidates

招聘代表性不足的 IT 求职者时的算法公平性

• DOI: 10.1108/oir-10-2018-0334
• 发表时间: 2019
• 期刊: Online Information Review
• 影响因子: 3.1
• 作者: Yarger, Lynette;Cobb Payton, Fay;Neupane, Bikalpa
• 通讯作者: Neupane, Bikalpa