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Pre-screening workers to overcome bias amplification in online labour markets

Author

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  • Ans Vercammen
  • Alexandru Marcoci
  • Mark Burgman

Abstract

Groups have access to more diverse information and typically outperform individuals on problem solving tasks. Crowdsolving utilises this principle to generate novel and/or superior solutions to intellective tasks by pooling the inputs from a distributed online crowd. However, it is unclear whether this particular instance of “wisdom of the crowd” can overcome the influence of potent cognitive biases that habitually lead individuals to commit reasoning errors. We empirically test the prevalence of cognitive bias on a popular crowdsourcing platform, examining susceptibility to bias of online panels at the individual and aggregate levels. We then investigate the use of the Cognitive Reflection Test, notable for its predictive validity for both susceptibility to cognitive biases in test settings and real-life reasoning, as a screening tool to improve collective performance. We find that systematic biases in crowdsourced answers are not as prevalent as anticipated, but when they occur, biases are amplified with increasing group size, as predicted by the Condorcet Jury Theorem. The results further suggest that pre-screening individuals with the Cognitive Reflection Test can substantially enhance collective judgement and improve crowdsolving performance.

Suggested Citation

  • Ans Vercammen & Alexandru Marcoci & Mark Burgman, 2021. "Pre-screening workers to overcome bias amplification in online labour markets," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-15, March.
    • Handle: RePEc:plo:pone00:0249051
    DOI: 10.1371/journal.pone.0249051
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    References listed on IDEAS

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    1. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
    2. Juliane E. Kämmer & Wolf E. Hautz & Stefan M. Herzog & Olga Kunina-Habenicht & Ralf H. J. M. Kurvers, 2017. "The Potential of Collective Intelligence in Emergency Medicine: Pooling Medical Students’ Independent Decisions Improves Diagnostic Performance," Medical Decision Making, , vol. 37(6), pages 715-724, August.
    3. Ans Vercammen & Yan Ji & Mark Burgman, 2019. "The collective intelligence of random small crowds: A partial replication of Kosinski et al. (2012)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(1), pages 91-98, January.
    4. Karsten Hueffer & Miguel A. Fonseca & Anthony Leiserowitz & Karen M. Taylor, 2013. "The wisdom of crowds: Predicting a weather and climate-related event," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 8(2), pages 91-105, March.
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