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SCC-IRG Track 1: Empowering and Enhancing Workers Through Building A Community-Centered Gig Economy

SCC-IRG 第 1 轨道：通过建立以社区为中心的零工经济，赋予工人权力并增强工人的能力

基本信息

批准号：
1952085
负责人：
Haiyi Zhu
金额：
$ 199.78万
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：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952085&HistoricalAwards=false
关键词：
SCC IRG Track Empowering Enhancing

项目摘要

The gig economy is characterized by short-term contract work performed by independent workers who are paid in return for the "gigs" they perform. Example gig platforms include Uber, Lyft, Postmates, Instacart, UpWork, and TaskRabbit. Gig economy platforms bring about more job opportunities, lower barriers to entry, and improve worker flexibility. However, growing evidence suggests that worker wellbeing and systematic biases on the gig economy platforms have become significant societal problems. For example most gig workers lack financial stability, have low earning efficiency and lack autonomy, and many have health issues due to long work hours and limited flexibility. Additionally, gig economy platforms perpetuate biases against already vulnerable populations in society. To address these problems, this project aims to build a community-centered, meta-platform to provide decision support and data sharing for gig workers and policymakers, in order to develop a more vibrant, healthy, and equitable gig economy.The project involves three major research activities. (1) Working with gig workers and local policymakers to understand their concerns, challenges, and considerations related to gig worker wellbeing, as well as the current practices, problems, and biases of existing gig economy platforms. (2) Developing a data-driven and human-centered decision-assistance environment to help gig workers make "smart" decisions in navigating and selecting gigs,and provide a macrolevel perspective for policymakers working to balance their diverse set of objectives and constraints. (3) Deploying and evaluating whether and how the above environment addresses the fundamental problems of worker wellbeing and systematic biases in the gig economy.This 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.

零工经济的特点是由独立工人从事短期合同工作，他们的报酬来自于他们所做的“零工”。零工平台的例子包括Uber、Lyft、PostMates、Insta、Upwork和TaskRabbit。零工经济平台带来更多就业机会，降低准入门槛，提高劳动者灵活性。然而，越来越多的证据表明，零工经济平台上的工人福祉和系统性偏见已经成为严重的社会问题。例如，大多数零工缺乏财务稳定性，收入效率低，缺乏自主权，许多人由于工作时间长和灵活性有限而存在健康问题。此外，零工经济平台延续了对社会中已经脆弱的人群的偏见。为了解决这些问题，本项目旨在建立一个以社区为中心的元平台，为零工人员和政策制定者提供决策支持和数据共享，以发展更具活力、健康和公平的零工经济。该项目涉及三项主要研究活动。(1)与零工和当地政策制定者合作，了解他们对零工福利的担忧、挑战和考虑，以及现有零工经济平台的当前做法、问题和偏见。(2)开发数据驱动和以人为中心的决策辅助环境，帮助零工人员在导航和选择零工时做出“明智”的决策，并为政策制定者提供宏观层面的视角，努力平衡他们多样化的目标和限制。(3)部署和评估上述环境是否以及如何解决零工经济中工人福祉和系统性偏差的根本问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

期刊论文数量（58）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

EgoSpeed-net: forecasting speed-control in driver behavior from egocentric video data

DOI：
10.1145/3557915.3560946
发表时间：
2022-09
期刊：
Proceedings of the 30th International Conference on Advances in Geographic Information Systems
影响因子：
0
作者：
Yichen Ding;Ziming Zhang;Yanhua Li;Xun Zhou
通讯作者：
Yichen Ding;Ziming Zhang;Yanhua Li;Xun Zhou

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

战略工具变量回归：从战略响应中恢复因果关系

DOI：
发表时间：
2022
期刊：
Proceedings of the 39th International Conference on Machine Learning
影响因子：
0
作者：
Harris, Keegan;Ngo, Dung Daniel;Stapleton, Logan;Heidari, Hoda;Wu, Steven
通讯作者：
Wu, Steven

MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning

DOI：
10.1145/3447548.3467238
发表时间：
2021-08
期刊：
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
影响因子：
0
作者：
Hu Ren;Sijie Ruan;Yanhua Li;Jie Bao;Chuishi Meng;Ruiyuan Li;Yu Zheng
通讯作者：
Hu Ren;Sijie Ruan;Yanhua Li;Jie Bao;Chuishi Meng;Ruiyuan Li;Yu Zheng

BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation