MicroLapse: Measuring workers' leniency to prediction errors of microtasks' working times

Susumu Saito, Teppei Nakano, Tetsunori Kobayashi, Jefrey P. Bigham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Working time estimation is known to be helpful for allowing crowd workers to select lucrative microtasks. We previously proposed a machine learning method for estimating the working times of microtasks, but a practical evaluation was not possible because it was unclear what errors would be problematic for workers across diferent scales of microtask working times. In this study, we formulate MicroLapse, a function that expresses a maximal error in working time prediction that workers can accept for a given working time length. We collected 60, 760 survey answers from 660 Amazon Mechanical Turk workers to formulate MicroLapse. Our evaluation of our previous method based on MicroLapse demonstrated that our working time prediction method was fairly successful for shorter microtasks, which could not have been concluded in our previous paper.

Original languageEnglish
Title of host publicationCSCW 2019 Companion - Conference Companion Publication of the 2019 Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Pages352-356
Number of pages5
ISBN (Electronic)9781450366922
DOIs
Publication statusPublished - 2019 Nov 9
Event22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2019 - Austin, United States
Duration: 2019 Nov 92019 Nov 13

Publication series

NameProceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Conference

Conference22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2019
CountryUnited States
CityAustin
Period19/11/919/11/13

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Human-Computer Interaction

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    Saito, S., Nakano, T., Kobayashi, T., & Bigham, J. P. (2019). MicroLapse: Measuring workers' leniency to prediction errors of microtasks' working times. In CSCW 2019 Companion - Conference Companion Publication of the 2019 Computer Supported Cooperative Work and Social Computing (pp. 352-356). (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW). Association for Computing Machinery. https://doi.org/10.1145/3311957.3359466