Scaling-Up the Crowd: Micro-Task Pricing Schemes for Worker Retention and Latency Improvement - Difallah, Djellel; Catasta, Michele; Demartini, Gianluca; Cudré-Mauroux, Philippe (Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2014)
Retaining workers on micro-task crowdsourcing platforms is essential in order to guarantee the timely completion of batches of Human Intelligence Tasks (HITs). Worker retention is also a necessary condition for the introduction of SLAs on crowdsourcing platforms. In this paper, we introduce novel pricing schemes aimed at improving the retention rate of workers working on long batches of similar tasks. We show how increasing or decreasing the monetary reward over time influences the number of tasks a worker is willing to complete in a batch, as well as how it influences the overall latency. We compare our new pricing schemes against traditional pricing methods (e.g., constant reward for all the HITs in a batch) and empirically show how certain schemes effectively function as an incentive for workers to keep working longer on a given batch of HITs. Our experimental results show that the best pricing scheme in terms of worker retention is based on punctual bonuses paid whenever the workers reach predefined milestones.