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:
2203212
负责人:
Norma Saiph Savage
金额:
$30.34万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-02-01 至 2024-01-31
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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Datavoidant: An AI System for Addressing Data Voids on Social Media
Datavoidant:用于解决社交媒体数据空白问题的人工智能系统
DOI:
--
发表时间:
2022
期刊:
Computer supported cooperative work CSCW
影响因子:
--
作者:
[Flores-Saviaga, C, Shangbin, F, Savage S]
通讯作者:
Savage S
DOI:
10.1145/3617694.3623235
发表时间:
2023-10
期刊:
Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
影响因子:
--
作者:
[Claudia Flores-Saviaga;Christopher Curtis;Saiph Savage]
通讯作者:
Claudia Flores-Saviaga;Christopher Curtis;Saiph Savage
4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling
第四届群体科学研讨会 - CANDLE:人类协作和数据标记学习算法
DOI:
10.1145/3539597.3572703
发表时间:
2023
期刊:
WSDM: ACM International Web Seach and Data Mining Conference 2023.
影响因子:
--
作者:
[Ustalov, Dmitry, Savage, Saiph, van Berkel, Niels, Liu, Yang]
通讯作者:
Liu, Yang
DOI:
10.1145/3491101.3503725
发表时间:
2022-04
期刊:
CHI Conference on Human Factors in Computing Systems Extended Abstracts
影响因子:
--
作者:
[Andy Alorwu;Saiph Savage;Niels van Berkel;Dmitry Ustalov;Alexey Drutsa;J. Oppenlaender;Oliver Bates;Danula Hettiachchi;U. Gadiraju;Jorge Gonçalves;S. Hosio]
通讯作者:
Andy Alorwu;Saiph Savage;Niels van Berkel;Dmitry Ustalov;Alexey Drutsa;J. Oppenlaender;Oliver Bates;Danula Hettiachchi;U. Gadiraju;Jorge Gonçalves;S. Hosio
La Independiente: Designing Ubiquitous Systems for Latin American and Caribbean Women Crowdworkers
La Independiente:为拉丁美洲和加勒比海女性众工设计无处不在的系统
DOI:
10.1145/3594739.3610728
发表时间:
2023
期刊:
ACM Ubicomp for All Symposium
影响因子:
--
作者:
[De Los Santos, Maya, Chávez, Norma Elva, Navarrete, Alberto, Martínez Pinto, Cristina, González, Luz Elena, Telles-Calderon, Tatiana, Savage, Saiph]
通讯作者:
Savage, Saiph
共 8 条
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
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批准号:1928528
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项目类别:Standard Grant
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资助金额:$30.34万
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财政年份:2019
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负责人:Norma Saiph Savage
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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: