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: 2326193
• 负责人: Julia Stoyanovich
• 金额: $ 72.18万
• 依托单位: New York 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=2326193&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）专家在组织中履行一系列关键的人员配置职能。 该项目的重点是支持技术和“大盒子”零售行业的人力资源专家，他们为入门级到中级职位寻找和筛选候选人。这些人力资源专家经常发现自己在填补职位空缺方面承受着巨大的压力，他们求助于自动决策系统（ADS）来管理人才招聘的细致平衡行为：筛选候选人库，以找到符合工作要求并具有“正确”文化契合度的人，同时遵守道德标准和法律的合规性。匹配和排名候选人的AI模型是这些ADS的核心。设计不良的模型可能会产生不正确和不一致的结果，无法将候选人与工作要求适当匹配，或者限制了合适候选人的可见性。这些问题加在一起，可能导致不负责任的决策过程和不公平的决策结果，伤害个人求职者和已经处于不利地位的社区成员，并使雇主面临诉讼的风险。这个项目重新想象人力资源专家（未来的工人）的角色，赋予他们与机构的理由，验证，审计，并影响ADS辅助招聘过程（未来的工作环境）。这些干预措施得到了名为Trapeze（未来技术）的人在环框架的支持，该框架支持人才招聘的透明自动化，沿着创新的教育材料和方法，培训人力资源专家更好地了解人工智能和ADS辅助决策的问责制。Trapeze的成果包括开源软件，允许广泛而多样化的负责任的人工智能研究人员和从业者社区更有效地构建和评估采购和筛选工具。该项目还促进了对技术和零售领域的人力资源专家做出ADS辅助决策的行为，社会，法律的和技术背景的理解。 本项目公开提供的培训材料和方法有助于人力资源专家在使用ADS时更加知情、负责、高效和有效。所有共享的材料，放在一起，作为一个强有力的蓝图，加强问责制，在ADS的使用在其他高风险的行业部门。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。