Collaborative Research: FW-HTF-RL: Trapeze: Responsible AI-assisted Talent Acquisition for HR Specialists

合作研究：FW-HTF-RL：Trapeze：负责任的人工智能辅助人力资源专家人才获取

• 批准号: 2326194
• 负责人: Frederick Oswald
• 金额: $ 49.5万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326194&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; RL

Human Resources (HR) Specialists fulfill a range of critical staffing functions in organizations. This project focuses on supporting HR Specialists in the technology and “big-box” retail industries, who source and screen candidates for entry- to mid-level positions. These HR Specialists often find themselves under enormous pressure to fill roles, and they turn to automated decision systems (ADS) for managing the meticulous balancing act of talent acquisition: sifting through pools of candidates to find people who meet job requirements and have the “right” culture fit, while adhering to ethical standards and legal compliance. AI models that match and rank candidates are at the heart of these ADS. Poorly designed models can produce incorrect and inconsistent results that fail to match candidates appropriately to job requirements, or that limit the visibility of well-suited candidates. Together, these problems can lead to unaccountable decision-making processes and unfair decision outcomes, harming individual job seekers and members of already disadvantaged communities, and putting employers at risk of litigation.This project reimagines the role of HR Specialists (future worker), empowering them with the agency to reason about, validate, audit, and influence the ADS-assisted hiring process (future work context). These interventions are supported by a human-in-the-loop framework called Trapeze (future technology) that supports transparent automation in talent acquisition, along with innovative educational materials and methodologies that train HR Specialists to become better informed about AI and accountability in ADS-assisted decisions. Outcomes of Trapeze include open-source software, allowing the broad and diverse community of responsible AI researchers and practitioners to build and evaluate tools for sourcing and screening more effectively. This project also advances the understanding of the behavioral, social, legal, and technical contexts in which HR Specialists in the technology and retail domains make ADS-assisted decisions. Publicly available training materials and methodologies from this project help HR Specialists become more informed, responsible, efficient, and effective in their use of ADS. All shared materials, taken together, serve as a strong blueprint for strengthening accountability in ADS use within other high-stakes sectors of industry.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.

人力资源（HR）专家履行了组织中的一系列关键人员配备功能。该项目着重于支持技术和“大型”零售行业的人力资源专家，后者来源和筛选了入门级至中级职位的候选人。这些人力资源专家通常会发现自己承受着巨大的扮演角色的压力，他们转向自动决策系统（ADS）来管理人才掌握的细致平衡行为：筛选候选人池以找到满足工作需求并具有“正确”文化的人，同时遵守义务标准和法律规定和法律规定。匹配和排名候选人的AI模型是这些广告的核心。设计较差的模型可能会产生不正确且不一致的结果，这些结果无法适当地与工作要求相匹配，或者限制了合适的候选人的可见性。 Together, these problems can lead to unaccountable decision-making processes and unfair decision outcomes, harming individual job seekers and members of already disadvantaged communities, and putting employees at risk of litigation.This project reimagines the role of HR Specialists (future workers), empowering them with the agency to reason about, validate, audit, and influence the ADS-assisted hiring process (future work context).这些干预措施得到了一个称为Trapeze（Future Technology）的人类框架框架的支持，该框架支持人才获取的透明自动化，以及创新的教育材料和方法，该材料和方法培训了人力资源专家，以更好地了解AI和责任感，并在ADS辅助决定中责任。空中飞人的结果包括开源软件，使负责任的AI研究人员和从业人员的广泛而多样化的社区可以更有效地构建和评估工具，以更有效地筛选。该项目还提高了对技术和零售领域的人力资源专家做出广告辅助决定的行为，社会，法律和技术环境的理解。该项目的公开培训材料和方法可帮助人力资源专家在使用广告方面变得更加知名，负责，高效和有效。所有共享的材料共同融合，都是强大的蓝图，用于加强行业其他高风险领域的ADS使用。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来评估NSF的法定任务。