FW-HTF-RL: Collaborative Research: Enabling Marginalized Rural and Urban Digital Workers to Collaborate with AI to Learn Skills, Increase Wages, and Access Creative Work

FW-HTF-RL：合作研究：让边缘化的农村和城市数字工人能够与人工智能合作学习技能、增加工资并获得创造性工作

• 批准号: 1928474
• 负责人: Chris Callison-Burch
• 金额: $ 37.47万
• 依托单位: University of Pennsylvania
• 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=1928474&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，当前平台上的工作通常是低水平的标签工作，提供的晋升机会很少。它通常旨在培训人工智能以自动化这项工作，而不是培训工人。该项目旨在提高工人的水平并改善在线工作市场，以便数字工作有助于传统产业退出地区的经济复苏。该项目旨在开发可持续的方法，将工人转移到高技能和创造性的数字工作，而这些工作在中短期内不太可能实现自动化。群体工作不仅可以改善雇主的工作成果，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四所大学的项目团队与当地机构合作，为工人提供培训，以逐步执行更先进的数字工作，同时赚钱。该项目的愿景是培养员工基本的计算机流畅性、使用人工智能工具以及最终的创新和创造力技能。这项工作是与农村合作伙伴（西弗吉尼亚州鲁珀特的鲁珀特公共图书馆）和城市合作伙伴（宾夕法尼亚州威尔金斯堡的 CommunityForge）合作完成的，并且还受益于与匹兹堡的博世公司、华盛顿特区的 ConservationX Labs 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高薪工作的人无法承担在发展技能所需的培训上花费的无薪时间。实现这一愿景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强工人而不是取代他们？（iii） ）如何设计工具来帮助工人培养技能和创造力，以应对未来不太可能实现自动化的工作？ 该项目有潜力在众包、人工智能、人机交互、认知科学、学习科学、社会学和经济学等各种相关领域取得进展。要同时提高工作成果和群体工作技能发展，就需要在更细致的层面上开发工人、技能及其轨迹模型。使工作人员能够与人工智能协作将需要新的人机交互范例。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。通过将调查建立在现实世界的背景下，该研究旨在获得可推广的知识，为人类人工智能前沿领域的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，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction

可视化显而易见的事物：用于名词属性预测的基于具体性的集成模型

• DOI: 10.48550/arxiv.2210.12905
• 发表时间: 2022-10-24
• 期刊: Contrast Media & Molecular Imaging
• 作者: Yue Yang;Artemis Panagopoulou;Marianna Apidianaki;Mark Yatskar;Chris Callison
• 通讯作者: Chris Callison

Turkish Judge: A Peer Evaluation Framework for Crowd Work Appeals

土耳其法官：群体工作上诉的同行评估框架

• 发表时间: 2020-07
• 期刊: Proceedings the AAAI Conference on Human Computation and Crowdsourcing
• 作者: Cohen, Edward;Venkateswaran, Mukund;Sankar, Nivedita;Callison
• 通讯作者: Callison

Visual Goal-Step Inference using wikiHow

使用 wikiHow 进行视觉目标步骤推理

• DOI: 10.18653/v1/2021.emnlp-main.165
• 发表时间: 2021-04-12
• 作者: Yue Yang;Artemis Panagopoulou;Qing Lyu;Li Zhang;Mark Yatskar;Chris Callison
• 通讯作者: Chris Callison

FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information

FIREBALL：具有结构化游戏状态信息的《龙与地下城》实战数据集

• DOI: 10.18653/v1/2023.acl-long.229
• 发表时间: 2023-05-02
• 作者: Andrew Zhu;Karmanya Aggarwal;Ale;er H. Feng;er;Lara J. Martin;Chris Callison
• 通讯作者: Chris Callison

Human-in-the-loop Schema Induction

人机交互模式归纳

• DOI: 10.18653/v1/2023.acl-demo.1
• 发表时间: 2023-02-25
• 期刊: ArXiv
• 作者: Tianyi Zhang;Isaac Tham;Zhaoyi Hou;J. Ren;Liyang Zhou;Hainiu Xu;Li Zhang;Lara J. Martin;Rotem Dror;Sha Li;Heng Ji;Martha Palmer;S. Brown;Reece Suchocki;Chris Callison
• 通讯作者: Chris Callison