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

美国许多农村地区面临着缺乏经济机会的问题。工作的未来可以通过在线工作和零工经济为农村和城市边缘社区带来机会。然而，在目前的平台上工作往往是低级别的标签工作，几乎没有提升的机会。它通常是为了训练人工智能来自动化这项工作，而不是培训工人。拟议中的项目旨在提振工人，改善在线工作的市场，以便数字工作可能有助于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，这些工作在短期到中期内不太可能实现自动化。群体工作可以转变为不仅为雇主改进工作产品，而且还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四所大学的项目团队与当地机构合作，为工人提供培训，让他们在赚钱的同时执行越来越先进的数字工作。该项目的愿景是通过基本的计算机流利程度，使用人工智能工具，最后是创新和创造力技能，为员工搭建脚手架。这项研究是与农村合作伙伴(鲁珀特公共图书馆，西弗吉尼亚州鲁珀特)和城市合作伙伴(宾夕法尼亚州威尔金斯堡的社区锻造)合作进行的，还受益于与匹兹堡的博世公司、华盛顿特区的保护X实验室和西弗吉尼亚州的合作伙伴关系。拟议中的研究解决了一个根本挑战，因为那些最需要发展技能以获得更高报酬工作的人负担不起开发这些工作所需的无偿培训时间。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工人过渡到在线工作？(Ii)人工智能工具如何增强工人，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实现自动化的工作培养技能和创造力？这个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。同时在群体工作中提高工作成果和技能发展，将需要在更微妙的水平上发展工人、技能及其轨迹的模型。使员工能够与人工智能协作将需要新的人机交互模式。支持创造力和发展新技能将需要探索新的组织和协调结构。通过将调查建立在现实世界的背景下，这项研究的目的是获得能够为研究人类-人工智能前沿的人群工作的未来奠定基础的概括性知识。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.