Using Worker Quality Scores to Improve Stopping Rules

Authors

  • Ittai Abraham Microsoft
  • Omar Alonso Microsoft
  • Vasilis Kandylas Microsoft
  • Rajesh Patel Microsoft
  • Steven Shelford Microsoft
  • Alex Slivkins Microsoft

Keywords:

stopping rules, crowdsourcing, quality control

Abstract

We consider the crowdsourcing task of learning the answer to simple multiple-choice microtasks. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same microtask. A stopping rule is an algorithm that for a given microtask decides for any given set of worker answers if the system should stop and output an answer or iterate and ask one more worker. A quality score for a worker is a score that reflects the historic performance of that worker. In this paper we investigate how to devise better stopping rules given such quality scores. We conduct a data analysis on a large-scale industrial crowdsourcing platform, and use the observations from this analysis to design new stopping rules that use the workers’ quality scores in a non-trivial manner. We then conduct a simulation based on a real-world workload, showing that our algorithm performs better than the more naive approaches.

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Published

2014-09-05

How to Cite

Abraham, I., Alonso, O., Kandylas, V., Patel, R., Shelford, S., & Slivkins, A. (2014). Using Worker Quality Scores to Improve Stopping Rules. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1). Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/13201