Crowdsourcing with Unsure Option

Machine Learning manuscript No. (will be inserted by the editor) Abstract One of the fundamental problems in crowdsourcing is the tradeoff between the number of the workers needed for high-accuracy aggregation and the budget to pay. For saving budget, it is important to ensure high quality of the crowd-sourced labels, hence the total cost on label collection will be reduced. Since the self-confidence of the workers often has a close relationship with their abilities, a possible way for quality control is to request the workers to return the labels only when they feel confident, by means of providing unsure option to them. On the other hand, allowing workers to choose unsure option also leads to the potential danger of budget waste. In this work, we propose the analysis towards understanding when providing the unsure option indeed leads to significant cost reduction, as well as how the confidence threshold is set. We also propose an online mechanism, which is alternative for threshold selection when the estimation of the crowd ability distribution is difficult. Keywords Crowdsourcing · Mechanism design · Unsure option · Cost reduction 1 Introduction Labeled data play a crucial role in machine learning. In recent years, crowdsourcing has been a popular cost-saving way for label collection.