FW-HTF-RL: Collaborative Research: Up-skilling and Re-skilling Marginalized Rural and Urban Digital Workers: AI-worker collaboration to access creative work

美国的许多农村地区面临着缺乏经济机会的问题。未来的工作可以通过在线工作和零工经济为农村和城市边缘化社区带来机会。然而，目前平台上的工作往往是低水平的标签工作，几乎没有晋升的机会。人们通常打算训练人工智能来自动化这项工作，而不是训练工人。拟议的项目旨在提升工人和改善在线工作的市场，以便数字工作可以帮助传统产业已经离开的地区的经济复苏。该项目旨在开发可持续的方法，使工人过渡到高技能和创造性的数字工作，这些工作在近期到中期内不太可能实现自动化。群体工作不仅可以为雇主改善工作产品，还可以帮助工人沿着未来工作所需的职业道路前进。来自卡内基梅隆大学，西弗吉尼亚大学，宾夕法尼亚州立大学和宾夕法尼亚大学与当地机构合作，为工人提供培训，以逐步完成更先进的数字工作，同时赚钱。该项目的愿景是通过基本的计算机流畅性，使用人工智能工具，最后是创新和创造力技能来帮助工人。这项工作是与农村合作伙伴（鲁珀特公共图书馆，在鲁珀特，西弗吉尼亚州）和城市合作伙伴（社区锻造在威尔金斯堡，宾夕法尼亚州），也受益于与博世公司的伙伴关系。在匹兹堡，华盛顿特区的ConservationX实验室和西弗吉尼亚州。拟议的研究解决了一个根本性的挑战，即那些最需要发展技能以获得更高收入工作的人负担不起发展技能所需的无薪培训时间。实现这一愿景将需要解决以下核心研究问题：（i）如何最好地支持边缘化工人向在线工作过渡？(ii)人工智能工具如何增加工人，而不是取代他们？(iii)如何设计工具来帮助工人为未来不太可能自动化的工作建立技能和创造力？ 该项目有可能在各种相互关联的领域取得进展，包括众包，人工智能，人机交互，认知科学，学习科学，社会学和经济学。要同时提高工作成果和人群工作中的技能发展，就需要在更细致的层面上开发工人、技能及其轨迹的模型。使工人能够与人工智能合作将需要新的人机交互模式。支持创造性和发展新技能将需要探索新的组织和协调结构。通过在真实的世界背景下进行调查，该研究旨在获得可推广的知识，为人类-人工智能前沿人群工作的未来研究奠定基础。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Many rural areas in the United States face a lack of economic opportunity. The future of work can bring opportunities for rural and urban marginalized communities through online work and the gig economy. However, work on current platforms is often low-level labeling work offering few opportunities for advancement. It is often intended to train Artificial Intelligence to automate this work away, instead of training workers. The proposed project aims to uplift workers and improve the marketplace for online work so that digital work may help with the economic recovery of regions whose traditional industries have left. This project aims to develop sustainable methods for transitioning workers to high-skilled and creative digital jobs that are unlikely to be automated in the near to medium term future. Crowd work can be transformed to not only improve the work product for the employer, but also to help the worker move along the career paths necessary for the future of work. The project team from four universities, Carnegie Mellon U., West Virginia U., Pennsylvania State University and University of Pennsylvania has partnered with local institutions to provide workers training to perform progressively more advanced digital work, while earning money. The vision of the project is to scaffold workers through basic computer fluency, working with AI tools, and finally innovation and creativity skills. This work is in collaboration with a rural partner (Rupert Public Library, in Rupert, WV) and urban partner (CommunityForge in Wilkinsburg, PA) and also benefits from a partnership with Bosch Inc. in Pittsburgh, ConservationX Labs in Washington DC, and the State of West Virginia.The proposed research addresses a fundamental challenge in that those who most need to develop skills to gain higher paying jobs cannot afford the unpaid time spent in training needed to develop them. Accomplishing this vision will require solving the following core research questions: (i) How can one best support the marginalized workers in their transition to online work?, (ii) How can Artificial Intelliegnce tools augment workers, rather than displace them?, (iii) How can tools be designed to help workers build skills and creativity for work that is unlikely to be automated in the future?. This project has the potential to make advances across a variety of interrelated fields including crowdsourcing, Artificial Intelligence, Human Computer Interaction, Cognitive Science, Learning Science, Sociology and Economics. Simultaneously enabling both improved work outcomes as well as skill development in crowd work will require the development of models of workers, skills, and their trajectories at a more nuanced level. Enabling workers to collaborate with Artificial Intelligence will require new human-computer interaction paradigms. Supporting creativity and the development of new skills will require the exploration of new organization and coordination structures. By grounding the investigations in real world contexts, the research aims for generalizable knowledge that can lay a foundation for research on the future of crowd work at the human-AI frontierThis 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.