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The Daemo crowdsourcing marketplace

  • Angela Richmond-Fuller
  • , S. S. Gaikwad
  • , M. Whiting
  • , D. Gamage
  • , C. A. Mullings
  • , D. Majeti
  • , S. Goyal
  • , A. Gilbee
  • , N. Chhibber
  • , A. Ginzberg
  • , A. Ballav
  • , A. Matin
  • , V. Sehgal
  • , T. Sarma
  • , A. Nasser
  • , J. Regino
  • , S. Zhou
  • , A. Stolzoff
  • , K. Mananova
  • , D. Dhakal
  • P. Srinivas, K. Ziulkoski, S. S. Niranga, M. Salih, A. Sinha, R. Vaish, M. S. Bernstein

    Research output: Chapter in Book/Report/Conference proceedingChapter

    16 Citations (Scopus)

    Abstract

    The success of crowdsourcing markets is dependent on a
    strong foundation of trust between workers and requesters. In current marketplaces, workers and requesters are often unable to trust each other’s quality, and their mental models of tasks are misaligned due to ambiguous instructions or confusing edge cases.

    This breakdown of trust typically arises from (1) flawed reputation systems which do not accurately reflect worker and requester quality, and from (2) poorly designed tasks. In this demo, we present how Boomerang and Prototype Tasks, the fundamental building blocks of the Daemo crowdsourcing marketplace, help restore trust between workers and requesters. Daemo’s Boomerang reputation system incentivizes alignment between opinion and ratings by determining the likelihood that workers and requesters will work together in the future based on how they rate each other. Daemo’s Prototype tasks require that new tasks go through a feedback iteration phase with a small number of workers so that requesters can revise their instructions and task designs before launch.
    Original languageEnglish
    Title of host publicationCSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
    PublisherAssociation for Computing Machinery
    ISBN (Print)9781450346887
    DOIs
    Publication statusPublished - Feb 2017

    Keywords

    • Crowdsourcing
    • Human computation

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