Crowdsourcing with Unsure Option
September 01, 2016 Β· Declared Dead Β· π Machine-mediated learning
"No code URL or promise found in abstract"
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Authors
Yao-Xiang Ding, Zhi-Hua Zhou
arXiv ID
1609.00292
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.HC
Citations
10
Venue
Machine-mediated learning
Last Checked
4 months ago
Abstract
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.
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