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

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。这个“融合加速器”第一阶段项目的更广泛影响/潜在利益涉及它将如何帮助美国工人、雇主和整体经济应对人工智能（AI）和相关自动化技术对未来工作的影响。据估计，到2030年，随着技术进步对工作领域的颠覆，全球约14%的劳动力可能需要改变职业类别。许多现在的工人和即将进入劳动力市场的人缺乏需求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，并规划他们的职业道路。我们的项目涉及人工智能、数据科学和优化、产业和组织心理学（具体而言是人-工作契合、人-环境契合、员工选择和偏见）以及人力资源（具体而言是劳动力培训和职业规划）的融合研究。它将利用与各种利益相关者的伙伴关系：政府组织、学术界、工业、民用和军事——为我们的研究提供信息，并确定适合过渡到实践的目标和可交付成果。“融合加速器”第一阶段项目包含三个要素，以转变决策支持，将工人与未来的工作联系起来，并为工作转换、培训和职业规划提供支持。首先，我们提出了一个丰富的、整体的、细粒度的工人特征以及适合由数据驱动的人工智能系统处理的工作、职业和交叉职业特征的视图。其次，我们捕捉到不同类型工作之间的相互关系，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谱的形式创建的数据驱动的详细模型，通过捕获工作的时间变化及其特征，为职业过渡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。预期的结果将产生一个知识图谱，对与不同职业相关的概念之间的关系有丰富的语义理解，一个帮助组织识别特定角色、职位和工作环境的工人的模型，以及一个职业规划工具包。随着人工智能和相关技术的进步，这些都与当前和未来劳动力面临的职业挑战有关。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.