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

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”一期项目的广泛影响/潜在效益是多方面的。众包创造了一个庞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，今天的众包平台并不能很好地支持众包工作者、求职者以及这个重要的在线劳动力市场的健康发展。这个项目是多个不同学科的研究和发展的融合，包括计算机科学、经济与商业、人文与社会科学。通过与众包平台提供商合作，开展快速发展的基础研究，该项目将提供可用于创建公平、道德、高效和可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、政府或人道主义援助组织在内的工作请求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。该项目将积极吸引来自弱势群体的学生，包括女性和少数民族学生。它将培养学生的研究能力和生产高质量成果的能力。它将通过发表研究论文和促进成果的广泛使用等活动广泛传播其成果。这个融合加速器第一阶段项目具有显著的智力价值。它解决了创建健康的众包劳动力市场的关键跨学科挑战，这对研究人员、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。我国劳动力市场规模庞大，发展迅速，但在公平、伦理、效率、诚信等方面存在着四大问题，且问题错综复杂。该项目通过与众包平台提供商合作，开展快速发展的基础研究，解决四个主要问题。它将(1)设计基于经济理论的激励结构，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，以安全保护众包工作者和工作请求者。它将把设计好的技术在客户端集成到web浏览器扩展中，在服务器端集成到一些工业合作伙伴的众包平台中。总体而言，它采用融合的方法来推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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.