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社区带来机会。然而，当前平台上的工作通常是低级别的标签工作，几乎没有晋升机会。它通常是为了训练人工智能来自动化这项工作，而不是训练工人。拟议中的项目旨在提升工人的地位，改善在线工作市场，从而使数字工作有助于传统工业消失的地区的经济复苏。该项目旨在开发可持续的方法，将工人转变为高技能和创造性的数字工作，这些工作在近期到中期不太可能实现自动化。群体性工作不仅可以改善雇主的工作成果，还可以帮助员工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逐步完成更高级的数字工作。该项目的愿景是通过基本的计算机熟练程度，使用人工智能工具，最终培养员工的创新和创造技能。这项工作是与农村合作伙伴（鲁珀特公共图书馆，在鲁珀特，西维吉尼亚州）和城市合作伙伴（社区forge在威尔金斯堡，宾夕法尼亚州），也受益于合作伙伴关系，博世公司在匹兹堡，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，即那些最需要培养技能以获得高薪工作的人，负担不起培养技能所需的无偿培训时间。实现这一愿景需要解决以下核心研究问题：(i)如何才能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强而不是取代工人？（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培养技能和创造力？这个项目有潜力在各种相关领域取得进展，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，为了在群体工作中提高工作成果和技能发展，需要在更细微的层面上开发工人、技能和他们的轨迹模型。使工人能够与人工智能协作将需要新的人机交互范式。支持创造力和新技能的发展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。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

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

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

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

Becoming the Super Turker:Increasing Wages via a Strategy from High Earning Workers

成为超级土耳其人：通过高收入工人的策略增加工资

• DOI: 10.1145/3366423.3380200
• 发表时间: 2020
• 期刊: The Web Conference
• 作者: Savage, Saiph;Chiang, Chun Wei;Saito, Susumu;Toxtli, Carlos;Bigham, Jeffrey
• 通讯作者: Bigham, Jeffrey