Convergence Accelerator Phase I (RAISE): AI-Based Decision Support for Linking Workers with Future Jobs and for Planning Work Transition and Career Pathway

融合加速器第一阶段 (RAISE)：基于人工智能的决策支持，用于将工人与未来工作联系起来并规划工作过渡和职业道路

• 批准号: 1936857
• 负责人: Nihar Mahapatra
• 金额: $ 40.31万
• 依托单位: Michigan State University
• 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: 2019-09-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936857&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; AI

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project relate to the ways in which it will help American workers, employers, and the overall economy face the impact of Artificial Intelligence (AI) and related automation technologies on the future of work. It is estimated that by 2030 about 14% of the global workforce may need to change occupational categories as the world of work is disrupted by technological advances. Many current workers and those entering the workforce lack skills that in-demand jobs and jobs of the future require. Addressing this challenge requires a holistic understanding of occupational categories and characteristics, their interrelationships and changes over time, worker characteristics, and how to connect workers to future jobs and help them transition from one type of work to another and plan their career pathway. Our project involves convergent research in AI, data science, and optimization, industrial and organizational psychology - specifically, person-job fit, person-environment fit, employee selection and bias, and human resources - specifically, workforce training and career planning. It will leverage partnerships with a diversity of stakeholders: government organizations, academia, industry, civilian, and military - to inform our research and define objectives and deliverables that are suitable for transitioning to practice.This Convergence Accelerator Phase I project incorporates three elements to transform decision support for linking workers with future jobs and for job transition, training, and career planning. First, we propose a rich, holistic, fine-grained view of worker characteristics and job, occupation, and cross-occupation characteristics suitable for processing by data-driven AI systems. Second, we capture interrelationships between different types of jobs as tasks and other characteristics can overlap or be closely related. Third, we propose a data-driven detailed model created in the form of an occupation knowledge graph to provide AI-based decision support in career transitions and planning, by capturing the temporal changes in jobs and their characteristics. We utilize various natural language processing, deep learning, information extraction, and optimization methods to develop these elements. The anticipated results will yield a knowledge graph with a rich semantic understanding of the relationship between concepts associated with different occupations, a model to assist organizations in identifying workers for particular roles, positions, and work contexts, and a career planning toolkit. These all tie into the occupational challenges that the current and future workforce faces with advances in AI and related technologies.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.

NSF融合加速器支持基于团队的多学科努力，这些努力应对国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。 该融合加速器I期项目的更广泛的影响/潜在好处与它将帮助美国工人，雇主和整体经济面对人工智能（AI）（AI）和相关自动化技术对工作未来的影响有关。据估计，到2030年，由于技术进步破坏了工作世界，大约14％的全球劳动力可能需要改变职业类别。许多现有工人和进入劳动力的工人缺乏需求工作和未来工作所需要的技能。应对这一挑战需要对职业类别和特征的整体理解，它们的相互关系和随着时间的变化，工人特征以及如何将工人连接到未来的工作，并帮助他们从一种工作过渡到另一种工作并计划其职业道路。我们的项目涉及在AI，数据科学和优化，工业和组织心理学方面的收敛研究，特别是个人工作，人与环境拟合，员工选择和偏见以及人力资源 - 特别是劳动力培训和职业计划。它将利用各种利益相关者的伙伴关系：政府组织，学术界，工业，平民和军事 - 为我们的研究提供了信息，并定义了适合过渡到实践的目标和可交付成果。这是Contergence Accelerator阶段I期项目，将三个要素结合在一起，以将决策者与未来的工作和职业培训联系起来，以将决策与工作人员联系起来。首先，我们提出了一种适合通过数据驱动的AI系统处理的工人特征和工作，职业和跨占领特征的丰富，整体，细粒度的观点。其次，我们捕获不同类型的作业之间的相互关系，因为任务和其他特征可能重叠或密切相关。第三，我们提出了一个以职业知识图的形式创建的数据驱动的详细模型，以通过捕获工作的时间变化及其特征来提供基于AI的决策支持。我们利用各种自然语言处理，深度学习，信息提取和优化方法来开发这些元素。预期的结果将产生一个知识图，具有对与不同职业相关的概念之间的关系的丰富语义理解，一个模型，以帮助组织确定特定角色，职位和工作环境的工人以及职业计划工具包。这一切都与当前和未来的劳动力面临的职业挑战相关联。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为NSF的法定任务值得通过评估。