Convergence Accelerator Phase I (RAISE): Toward Fair, Ethical, Efficient, and Trustworthy Crowdsourcing Platforms to Support Crowdworkers in Jobs of the Future

融合加速器第一阶段（RAISE）：建立公平、道德、高效和值得信赖的众包平台，以支持众包工作者的未来工作

• 批准号: 1936968
• 负责人: Chuan Yue
• 金额: $ 100万
• 依托单位: Colorado School of Mines
• 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: 2019-09-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936968&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Toward

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is multifaceted. Crowdsourcing has created a vast and rapidly growing online labor market. However, today's crowdsourcing platforms cannot well support crowdworkers, job requesters, and the healthy growth of this important online labor market due to four major problems: fairness, ethics, efficiency, and trustworthiness. This project is a convergence of the research and development from multiple intellectually distinct disciplines including Computer Science, Economics & Business, and Humanities & Social Sciences. By performing fundamental research with rapid development advances through partnerships with crowdsourcing platform providers, this project will deliver techniques that can be used to create fair, ethical, efficient, and trustworthy crowdsourcing platforms to support American crowdworkers. It will also enable job requesters including researchers, companies, and government or humanitarian aid organizations to receive high-quality and trustworthy task submissions for them to confidently conduct their important studies and make important decisions. This project will actively involve students from underrepresented groups including female and minority students. It will train students on research and on producing high-quality deliverables. It will widely disseminate its results via activities such as publishing research papers and promoting the wide use of the deliverables.This Convergence Accelerator Phase I project has significant intellectual merit. It addresses the critical interdisciplinary challenges of creating a healthy crowdsourcing labor market that is crucial to the important studies, computations, and decisions of researchers, companies, as well as government and humanitarian aid organizations. This labor market is vast and rapidly growing, but has four major problems intertwined from the fairness, ethics, efficiency, and trustworthiness perspectives in a very complicated manner. This project addresses the four major problems by performing fundamental research with rapid development advances through partnerships with crowdsourcing platform providers. It will (1) design incentive structures based on economic theory to incentivize fairness in crowdsourcing, (2) design research, training, and assessment mechanisms to incorporate ethics into crowdsourcing, (3) design machine learning models to improve the efficiency of crowdworkers, and (4) design machine learning models to securely protect both crowdworkers and job requesters. It will integrate the designed techniques at the client-side into a web browser extension, and at the server-side into some industrial partner's crowdsourcing platform. Overall, it takes a convergence approach to advance the scientific knowledge and understanding of crowdsourcing and its closely related disciplines including economics, business, humanities, social sciences, and computer science.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.

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。此融合加速器第一阶段项目的更广泛影响/潜在好处是多方面的。众包创造了一个巨大且快速增长的在线劳动力市场。然而，由于公平、道德、效率和诚信四大问题，如今的众包平台无法很好地支持众创人员、求职者，以及这个重要的在线劳动力市场的健康成长。该项目汇集了多个不同学科的研究与开发成果，包括计算机科学、经济学与商业、人文与社会科学。通过与众包平台提供商合作开展基础研究和快速发展，该项目将提供可用于创建公平、道德、高效和值得信赖的众包平台的技术，以支持美国的众包工作者。它还将使包括研究人员、公司和政府或人道主义援助组织在内的求职者能够收到高质量和值得信赖的任务提交，使他们能够自信地进行重要研究并做出重要决策。该项目将积极吸收来自代表性不足群体的学生，包括女学生和少数族裔学生。它将培训学生进行研究和生产高质量的交付成果。它将通过发表研究论文和促进成果的广泛使用等活动来广泛传播其成果。这一融合加速器第一阶段项目具有重大的智力价值。它解决了创建一个健康的众包劳动力市场的关键跨学科挑战，这个市场对研究人员、公司以及政府和人道主义援助组织的重要研究、计算和决策至关重要。这个劳动力市场规模巨大，增长迅速，但从公平、道德、效率和诚信的角度来看，四大问题错综复杂地交织在一起。该项目通过与众包平台提供商建立伙伴关系，以快速发展的方式进行基础研究，解决了四个主要问题。它将(1)设计基于经济学理论的激励结构，以激励众包中的公平；(2)设计研究、培训和评估机制，将伦理纳入众包；(3)设计机器学习模型，以提高众包工作者的效率；(4)设计机器学习模型，以安全地保护众包工作者和求职者。它将在客户端将设计的技术集成到Web浏览器扩展中，并在服务器端集成到某个行业合作伙伴的众包平台中。总体而言，它采用融合的方法来促进对众包及其密切相关学科(包括经济学、商学、人文科学、社会科学和计算机科学)的科学知识和理解。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

Quality Control in Crowdsourcing based on Fine-Grained Behavioral Features

基于细粒度行为特征的众包质量控制

• DOI: 10.1145/3479586
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Pei, Weiping;Yang, Zhiju;Chen, Monchu;Yue, Chuan
• 通讯作者: Yue, Chuan

Visualizing and Interpreting RNN Models in URL-based Phishing Detection

基于 URL 的网络钓鱼检测中 RNN 模型的可视化和解释

• DOI: 10.1145/3381991.3395602
• 发表时间: 2020
• 期刊: ACM Symposium on Access Control Models and Technologies
• 作者: Feng, Tao;Yue, Chuan
• 通讯作者: Yue, Chuan

A Comparative Measurement Study of Web Tracking on Mobile and Desktop Environments

移动和桌面环境下网络跟踪的比较测量研究

• DOI: 10.2478/popets-2020-0016
• 发表时间: 2020
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Yang, Zhiju;Yue, Chuan
• 通讯作者: Yue, Chuan

Crowdsourcing as a Tool for Research: Methodological, Fair, and Political Considerations

• DOI: 10.1177/02704676211003808
• 发表时间: 2020-10
• 期刊: Bulletin of Science, Technology & Society
• 作者: Stephen C. Rea;Hanzelle Kleeman;Qin Zhu;Benjamin Gilbert;Chuan Yue

WTAGRAPH: Web Tracking and Advertising Detection using Graph Neural Networks

• DOI: 10.5281/zenodo.5166790
• 发表时间: 2021-08
• 期刊: 2022 IEEE Symposium on Security and Privacy (SP)
• 作者: Zhiju Yang;Weiping Pei;Mon-Chu Chen;Chuan Yue