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，这些努力应对国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。 该收敛加速器I期项目的更广泛的影响/潜在优势是多方面的。 众包创造了一个广阔而迅速增长的在线劳动力市场。 但是，由于四个主要问题：公平，道德，效率和可信赖性，当今的众包平台不能很好地支持众劳工，工作请求者以及这个重要的在线劳动力市场的健康增长。 该项目是来自多个智力上不同学科的研究和发展的融合，包括计算机科学，经济学和商业以及人文与社会科学。 通过与众包平台提供商的合作伙伴关系，通过快速发展进行基础研究，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，以支持美国人群工作者。 它还将使包括研究人员，公司，政府或人道主义援助组织在内的求职者能够为他们提供高质量和值得信赖的任务提交，以自信地进行重要的研究并做出重要的决定。 该项目将积极参与来自代表性不足的团体（包括女性和少数民族学生）的学生。 它将培训学生研究并生产高质量的可交付成果。 它将通过诸如发布研究论文和促进可交付成果的广泛使用之类的活动来广泛传播其结果。该融合加速器I期项目具有重要的知识分子优点。 它解决了建立健康的众包劳动力市场的关键跨学科挑战，这对于研究人员，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。这个劳动力市场庞大且迅速增长，但与公平，伦理，效率和可信赖的观点相关的四个主要问题以非常复杂的方式交织在一起。 该项目通过与众包平台提供商的合作伙伴关系进行基础研究来解决这四个主要问题。 它将（1）基于经济理论设计激励结构，以激励众包中的公平性，（2）设计研究，培训和评估机制，将道德融入众包中，（3）设计机器学习模型以提高人群的效率，以及（4）设计机器学习模型以安全地保护拥挤者和工作要求。 它将将客户端的设计技术集成到Web浏览器扩展程序中，并将服务器端集成到某些工业合作伙伴的众包平台中。 总体而言，它采用了一种融合方法来促进对众包的科学知识和理解及其密切相关的学科，包括经济学，商业，人文科学，社会科学和计算机科学。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。

项目成果

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

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

Attention Please: Your Attention Check Questions in Survey Studies Can Be Automatically Answered

• DOI: 10.1145/3366423.3380195
• 发表时间: 2020-01-01
• 期刊: WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
• 作者: Pei, Weiping;Mayer, Arthur;Yue, Chuan
• 通讯作者: Yue, Chuan

Attacking and Protecting Tunneled Traffic of Smart Home Devices

• DOI: 10.1145/3374664.3375723
• 发表时间: 2020-01-01
• 期刊: PROCEEDINGS OF THE TENTH ACM CONFERENCE ON DATA AND APPLICATION SECURITY AND PRIVACY, CODASPY 2020
• 作者: Alshehri, Ahmed;Granley, Jacob;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