Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
This addresses the fundamental challenge of data quality in crowdsourcing for machine learning applications, offering a novel incentive mechanism with practical benefits.
The paper tackles the problem of low-quality data in crowdsourcing by proposing a simple payment mechanism that incentivizes workers to answer only questions they are sure of, showing it is the only incentive-compatible mechanism under a 'no-free-lunch' requirement and leads to a significant drop in error rates in experiments with over 900 worker-task pairs.
Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental challenge in crowdsourcing, we propose a simple payment mechanism to incentivize workers to answer only the questions that they are sure of and skip the rest. We show that surprisingly, under a mild and natural "no-free-lunch" requirement, this mechanism is the one and only incentive-compatible payment mechanism possible. We also show that among all possible incentive-compatible mechanisms (that may or may not satisfy no-free-lunch), our mechanism makes the smallest possible payment to spammers. We further extend our results to a more general setting in which workers are required to provide a quantized confidence for each question. Interestingly, this unique mechanism takes a "multiplicative" form. The simplicity of the mechanism is an added benefit. In preliminary experiments involving over 900 worker-task pairs, we observe a significant drop in the error rates under this unique mechanism for the same or lower monetary expenditure.