A Quality Assuring Multi-Armed Bandit Crowdsourcing Mechanism with Incentive Compatible Learning

We develop a novel multi-armed bandit (MAB) mechanism for the problem of selecting a subset of crowd workers to achieve an assured accuracy for each binary labelling task in a cost optimal way. This problem is challenging because workers have unknown qualities and strategic costs.