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

FW-HTF-RL：协作研究：边缘化农村和城市数字工人的技能提升和再培训：人工智能与工人协作以获得创造性工作

• 批准号: 1928631
• 负责人: Jeffrey Bigham
• 金额: $ 145.48万
• 依托单位: Carnegie-Mellon 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: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928631&HistoricalAwards=false
• 关键词: FW; HTF; RL; Collaborative; Research

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.

美国许多农村地区面临缺乏经济机会。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化社区带来机会。但是，在当前平台上的工作通常是低级标签工作，提供了很少的进步机会。通常打算培训人工智能以使这项工作自动化，而不是培训工人。拟议的项目旨在提升工人并改善在线工作的市场，以便数字工作可以帮助传统行业离开的地区的经济复苏。该项目旨在开发可持续的方法，以将工人转变为高技能和创造性的数字作业，而这些数字工作不太可能在中期未来自动化。可以改变人群的工作，不仅可以改善雇主的工作产品，还可以帮助工人沿着未来的工作所必需的职业道路行驶。来自四所大学的项目团队卡内基·梅隆U.，西弗吉尼亚州，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，以逐步进行更先进的数字工作，同时赚钱。该项目的愿景是通过基本的计算机流利度，使用AI工具以及最终创新和创造力的脚手架工人。这项工作与农村合作伙伴（鲁珀特公共图书馆，西弗吉尼亚州）和城市合作伙伴（宾夕法尼亚州威尔金斯堡的社区伙伴）合作，还受益于与Bosch Inc.在匹兹堡的Bosch Inc.的合作伙伴关系中的好处需要开发它们。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过渡到在线工作的？ 该项目有可能在各种相互关联的领域取得进步，包括众包，人工智能，人类计算机互动，认知科学，学习科学，社会学和经济学。同时，可以提高工作成果以及人群工作中的技能发展，将需要更加细微的水平发展工人，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类计算机互动范例。支持创造力和新技能的发展将需要探索新的组织和协调结构。通过在现实世界背景下进行调查，该研究的目的是概括知识，可以为人类临时奖的人群工作的未来奠定基础，这反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响标准通过评估来进行评估。

项目成果

Tech Help Desk: Support for Local Entrepreneurs Addressing the Long Tail of Computing Challenges

技术服务台：支持当地企业家应对计算挑战的长尾问题

• DOI: 10.1145/3491102.3517708
• 发表时间: 2022
• 期刊: CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
• 作者: Kotturi, Yasmine;Johnson, Herman T;Skirpan, Michael;Fox, Sarah E;Bigham, Jeffrey P;Pavel, Amy
• 通讯作者: Pavel, Amy

Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making

• DOI: 10.1145/3491102.3501968
• 发表时间: 2022-02
• 期刊: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
• 作者: Michael Xieyang Liu;A. Kittur;B. Myers

Fuse: In-Situ Sensemaking Support in the Browser

• DOI: 10.1145/3526113.3545693
• 发表时间: 2022-08
• 期刊: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
• 作者: Andrew Kuznetsov;Joseph Chee Chang;Nathan Hahn;Napol Rachatasumrit;Bradley Breneisen;Julina Coupland;A. Kittur

Templates and Trust-o-meters: Towards a widely deployable indicator of trust in Wikipedia

模板和信任计：在维基百科中建立一个可广泛部署的信任指标

• DOI: 10.1145/3491102.3517523
• 发表时间: 2022
• 期刊: CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
• 作者: Kuznetsov, Andrew;Novotny, Margeigh;Klein, Jessica;Saez-Trumper, Diego;Kittur, Aniket
• 通讯作者: Kittur, Aniket

Tabs.do: Task-Centric Browser Tab Management

Tabs.do：以任务为中心的浏览器选项卡管理

• DOI: 10.1145/3472749.3474777
• 发表时间: 2021
• 期刊: UIST '21: Proceedings of the 2022 ACM Conference on User Interface Software and Technology
• 作者: Chang, Joseph Chee;Kim, Yongsung;Miller, Victor;Liu, Michael Xieyang;Myers, Brad A;Kittur, Aniket
• 通讯作者: Kittur, Aniket