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

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛影响/潜在好处与它将如何帮助美国工人、雇主和整体经济面对人工智能(AI)和相关自动化技术对未来工作的影响有关。据估计，到2030年，大约14%的全球劳动力可能需要改变职业类别，因为技术进步扰乱了工作世界。许多目前的工人和进入劳动力大军的人缺乏紧缺工作和未来工作所需的技能。应对这一挑战需要全面了解职业类别和特征、它们之间的相互关系和随时间的变化、工人的特征，以及如何将工人与未来的工作联系起来，帮助他们从一种工作类型过渡到另一种工作类型，并规划自己的职业道路。我们的项目涉及人工智能、数据科学和优化、工业和组织心理学-具体地说，人与工作匹配、人与环境匹配、员工选择和偏见以及人力资源-具体地说，劳动力培训和职业规划方面的融合研究。它将利用与不同利益相关者(政府组织、学术界、工业界、民间和军方)的合作伙伴关系，为我们的研究提供信息，并定义适合过渡到实践的目标和交付成果。该融合加速器第一阶段项目包含三个要素，以转变决策支持，将员工与未来的工作联系起来，并进行工作过渡、培训和职业规划。首先，我们提出了适合于数据驱动的人工智能系统处理的丰富、整体、细粒度的工人特征以及工作、职业和跨职业特征的视图。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以重叠或密切相关。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，通过捕捉工作及其特征的时间变化，为职业转变和规划提供基于人工智能的决策支持。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。预期结果将产生一个对与不同职业相关的概念之间的关系有丰富语义理解的知识图谱、一个协助各组织为特定角色、职位和工作环境确定工作人员的模型，以及一个职业规划工具包。这些都与当前和未来劳动力面临的随着人工智能和相关技术的进步而面临的职业挑战有关。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.