A clarity and fairness aware framework for selecting workers in competitive crowdsourcing tasks

Abstract

Crowdsourcing is a powerful technique for accomplishing tasks that are difficult for machines but easy for humans. However, ensuring the quality of the workers who participate in the task is a major challenge. Most of the existing studies have focused on selecting suitable workers based on their attributes and the task requirements, while neglecting the requesters’ characteristics as a key factor in the crowdsourcing process. In this paper, we address this gap by considering the requesters’ preferences and behavior in crowdsourcing systems with competition, where the requester chooses only one worker’s contribution as the final answer. A model is proposed in which the requesters’ characteristics are taken into consideration when finding suitable workers. Also, we propose new definitions for clarity and the fairness of requesters and propose models and formulations to employ them, alongside task and workers’ attributes, to find more suitable workers. We have evaluated the efficacy of our proposed model by analyzing a real-world dataset and compared it with two current state-of-the-art approaches. Our results demonstrate the superiority of our proposed method in assigning the most suitable workers.