Data crowdsourcing has found a broad range of applications (e.g., environmental monitoring and image classification) by leveraging the “wisdom” of a potentially large crowd of “workers” (e.g., mobile users). A key metric of crowdsourcing is data accuracy, which relies on the quality of the participating workers’ data (e.g., the probability that the data are equal to the ground truth). However, the data quality of a worker can be its own private information (which the worker learns, e.g., based on its location) that it may have incentive to misreport, which can, in turn, mislead the crowdsourcing requester about the accuracy of the data. This issue is further complicated by the fact that the worker can also manipulate its effort made in the crowdsourcing task and the data reported to the requester, which can also mislead the requester. In this paper, we devise truthful crowdsourcing mechanisms for quality, effort, and data elicitation (QEDE) , which incentivize strategic workers to truthfully report their private worker quality and data to the requester, and make truthful effort as desired by the requester. The truthful design of the QEDE mechanisms overcomes the lack of ground truth and the coupling in the joint elicitation of the worker quality, effort, and data. Under the QEDE mechanisms, we characterize the socially optimal and the requester's optimal (RO) task assignments, and analyze their performance. We show that the RO assignment is determined by the largest “virtual quality” rather than the highest quality among workers, which depends on the worker's quality and the quality's distribution. We evaluate the QEDE mechanisms using simulations that demonstrate the truthfulness of the mechanisms and the performance of the optimal task assignments.

中文翻译：

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真正的数据质量启发，实现质量感知的数据众包

通过利用潜在的大量“工人”（例如移动用户）的“智慧”，数据众包已发现了广泛的应用（例如环境监控和图像分类）。众包的关键指标是数据准确性，这取决于质量参与人员的数据（例如，数据等于基本事实的概率）。但是，工作人员的数据质量可以是其自己的私人信息（例如，工作人员根据其所在位置得知），从而可能导致误报，从而反过来可能误导向众包请求者提供有关数据准确性的信息。由于工人还可以操纵其在众包任务中所做的努力和报告给请求者的数据，这也使该请求者产生误解，从而使这一问题更加复杂。在本文中，我们设计了如实的众包机制质量，工作量和数据启发（QEDE） ，这会激励战略工作者向请求者真实地报告其私人工作者的素质和数据，并根据请求者的要求进行如实的努力。QEDE机制的真实设计克服了缺乏基础事实的缺点，并克服了工人素质，工作量和数据的共同启发。在QEDE机制下，我们表征社会最优和请求者的最优（RO）任务分配，并分析其绩效。我们证明，RO分配是由最大的“虚拟质量”而不是工人中的最高质量决定的，后者取决于工人的质量和质量的分布。我们使用模拟来评估QEDE机制，这些模拟演示了机制的真实性和最佳任务分配的性能。