Deja Vu: Characterizing Worker Reliability Using Task Consistency

Authors

  • Alex Williams University of Waterloo
  • Joslin Goh University of Waterloo
  • Charlie Willis Harvard University
  • Aaron Ellison Harvard University
  • James Brusuelas University of Oxford
  • Charles Davis Harvard University
  • Edith Law University of Waterloo

DOI:

https://doi.org/10.1609/hcomp.v5i1.13307

Keywords:

consistency, reliability, crowdsourcing

Abstract

Consistency is a practical metric that evaluates an instrument's reliability based on its ability to yield the same output when repeatedly given a particular input. Despite its broad usage, little is understood about the feasibility of using consistency as a measure of worker reliability in crowdwork. In this paper, we explore the viability of measuring a worker's reliability by their ability to conform to themselves. We introduce and describe Deja Vu, a mechanism for dynamically generating task queues with consistency probes to measure the consistency of workers who repeat the same task twice. We present a study that utilizes Deja Vu to examine how generic characteristics of the duplicate task - such as placement, difficulty, and transformation - affect a worker’s task consistency in the context of two unique object detection tasks. Our findings provide insight into the design and use of consistency-based reliability metrics.

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Published

2017-09-21

How to Cite

Williams, A., Goh, J., Willis, C., Ellison, A., Brusuelas, J., Davis, C., & Law, E. (2017). Deja Vu: Characterizing Worker Reliability Using Task Consistency. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 5(1), 197-205. https://doi.org/10.1609/hcomp.v5i1.13307