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) 专家在组织中履行一系列关键的人员配置职能，该项目重点支持技术和“大型”零售行业的人力资源专家，他们负责寻找和筛选初级到中级职位的候选人。人力资源专家经常发现自己面临着填补职位空缺的巨大压力，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，找到符合工作要求并拥有“合适”职位的人文化契合，同时坚持道德标准和法律合规性是这些 ADS 的核心，设计不当的模型可能会产生不正确且不一致的结果，无法将候选人与工作要求进行适当匹配，或者限制合适候选人的可见性。总之，这些问题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。该项目重新构想了人力资源专家（未来工人）的角色，赋予他们权力他们与该机构负责推理、验证、审核和影响 ADS 辅助的招聘流程（未来的工作环境），这些干预措施由名为 Trapeze（未来技术）的人机交互框架提供支持，该框架支持透明的人才自动化。 Trapeze 的成果包括开源软件，使负责的人工智能研究人员和从业者能够建立广泛而多样化的社区。和评估工具该项目还增进了对行为、社会、法律和技术背景的理解，在这些背景下，技术和零售领域的人力资源专家可以通过该项目公开提供的培训材料和方法来帮助人力资源部门做出 ADS 辅助决策。专家在使用 ADS 时变得更加知情、负责、高效和有效。所有共享材料共同构成了加强其他高风险行业领域 ADS 使用问责制的强大蓝图。该奖项反映了 NSF 的法定使命。并且已经通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。