An Unbiased and Time Efficient Task Allocation for Crowdsourcing Systems

Crowdsourcing systems are platforms that mediate between a task requester and a solution provider by allocating tasks to workers based on their latent ability of completing the task. In crowdsourcing, platforms are biased towards expert and experienced workers who guarantee solutions. This affect ability of inexpert workers or new comers to get a head start on the platform. An unbiased task allocation paves the way for a new comer to gain recognition on the crowdsourcing platform. In this work, we propose a task allocation model using a worker skill set evaluation method. Given the worker profiles and task specifications we allocate tasks on the basis of strength factor of the worker. We improve the overhead time of the process without compromising on the qualitative requirements. The proposed system is compared with the existing task allocation methods. New comers on the platform are successfully given an opportunity to demonstrate their abilities.