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: Collaborative Research: Enabling Marginalized Rural and Urban Digital Workers to Collaborate with AI to Learn Skills, Increase Wages, and Access Creative Work
批准号:
1928474
负责人:
Chris Callison-Burch
金额:
$37.47万
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
项目状态:
已结题
起止时间:
2019-09-15 至 2024-08-31
中文摘要
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英文摘要
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.
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DOI:
10.1109/cvpr52729.2023.01839
发表时间:
2022-11
期刊:
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子:
--
作者:
[Yue Yang;Artemis Panagopoulou;Shenghao Zhou;Daniel Jin;Chris Callison-Burch;Mark Yatskar]
通讯作者:
Yue Yang;Artemis Panagopoulou;Shenghao Zhou;Daniel Jin;Chris Callison-Burch;Mark Yatskar
Goal-Oriented Script Construction
面向目标的脚本构建
DOI:
--
发表时间:
2021
期刊:
Proceedings of the 14th International Conference on Natural Language Generation
影响因子:
--
作者:
[Lyu, Qing, Zhang, Li, Callison-Burch Chris]
通讯作者:
Callison-Burch Chris
DOI:
10.48550/arxiv.2210.12905
发表时间:
2022-10
期刊:
影响因子:
--
作者:
[Yue Yang;Artemis Panagopoulou;Marianna Apidianaki;Mark Yatskar;Chris Callison-Burch]
通讯作者:
Yue Yang;Artemis Panagopoulou;Marianna Apidianaki;Mark Yatskar;Chris Callison-Burch
DOI:
10.18653/v1/2021.emnlp-main.500
发表时间:
2021-09
期刊:
ArXiv
影响因子:
--
作者:
[Joongwon Kim;Mounica Maddela;Reno Kriz;Wei Xu;Chris Callison-Burch]
通讯作者:
Joongwon Kim;Mounica Maddela;Reno Kriz;Wei Xu;Chris Callison-Burch
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
DOI:
--
发表时间:
2020
期刊:
Proceedings the AAAI Conference on Human Computation and Crowdsourcing
影响因子:
--
作者:
[Cohen, Edward, Venkateswaran, Mukund, Sankar, Nivedita, Callison-Burch, Chris]
通讯作者:
Callison-Burch, Chris
共 23 条
EAGER: Simplification as Machine Translation
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批准号:1430651
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项目类别:Standard Grant
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资助金额:$9.97万
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财政年份:2014
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负责人:Chris Callison-Burch
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依托单位:
国内基金
海外基金
转HTFα对脊髓继发性损伤和微循环重建的影响
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批准号:39970755
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项目类别:面上项目
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资助金额:13.0万元
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批准年份:1999
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负责人:毛伯镛
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依托单位: