Understanding the Crowd: Ethical and Practical Matters in the Academic Use of Crowdsourcing

Ernestasia Siahaan, David Martin, Sheelagh Carpendale, Neha Gupta*, Tobias Hoßfeld, Babak Naderi, Judith Redi, Ernestasia Siahaan, Ina Wechsung

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

11 Citations (Scopus)


The driving force behind digital crowdsourcing are its workers: working, hidden behind the scenes, churning out data in experiments, participating in research studies, completing little tasks to accomplish HITs online. Understanding workers and crowdwork better is therefore key to develop a more effective and fair use of crowdsourcing for research. This chapter attempts to help develop an understanding of the various aspects of the crowd by drawing parallels between workers of different platforms (AMT, Microworkers and Crowdee) through quantitative and qualitative analysis of current and newly collected data. A picture of the crowd is drawn by uncovering their motivations, workplaces, skills and infrastructure, issues and perspectives about the design of microtasks, the employers and the microtask-based platforms. Legal and ethical perspectives on crowdwork are also discussed, and online resources are reviewed that researchers can use as a primer to employ crowdworkers in an ethical and fair way. The chapter provides information, a review of internationally recognised ethical principles and practical advice to those who would like to use crowdsourcing for experiments and to carry out research studies as an informed researcher and crowd employer.

Original languageEnglish
Title of host publicationEvaluation in the Crowd.
Subtitle of host publicationCrowdsourcing and Human-Centered Experiments
EditorsDaniel Archambault, Helen Purchase, Tobias Hoßfeld
Place of PublicationCham
Number of pages43
ISBN (Electronic)978-3-319-66435-4
ISBN (Print)978-3-319-66434-7
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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