Crowdsourcing with Unsure Option
This work addresses budget efficiency in crowdsourcing for researchers and practitioners, but it is incremental as it builds on existing quality control methods.
The paper tackles the trade-off between worker count and budget in crowdsourcing by analyzing when providing an 'unsure' option reduces costs and how to set confidence thresholds, proposing an online mechanism for threshold selection when crowd ability distribution is hard to estimate.
One of the fundamental problems in crowdsourcing is the trade-off 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.