DocumentCode :
2249678
Title :
Mini-crowdsourcing end-user assessment of intelligent assistants: A cost-benefit study
Author :
Shinsel, Amber ; Kulesza, Todd ; Burnett, Margaret ; Curran, William ; Groce, Alex ; Stumpf, Simone ; Wong, Weng-Keen
Author_Institution :
Oregon State Univ., Corvallis, OR, USA
fYear :
2011
fDate :
18-22 Sept. 2011
Firstpage :
47
Lastpage :
54
Abstract :
Intelligent assistants sometimes handle tasks too important to be trusted implicitly. End users can establish trust via systematic assessment, but such assessment is costly. This paper investigates whether, when, and how bringing a small crowd of end users to bear on the assessment of an intelligent assistant is useful from a cost/benefit perspective. Our results show that a mini-crowd of testers supplied many more benefits than the obvious decrease in workload, but these benefits did not scale linearly as mini-crowd size increased - there was a point of diminishing returns where the cost-benefit ratio became less attractive.
Keywords :
cost-benefit analysis; intelligent design assistants; program testing; cost-benefit ratio; cost-benefit study; diminishing returns; intelligent assistants; mini-crowd size; mini-crowdsourcing end-user assessment; systematic assessment; Analysis of variance; Educational institutions; Reliability; Software; Software testing; Systematics; crowdsourcing; end-user programming; machine learning; testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Visual Languages and Human-Centric Computing (VL/HCC), 2011 IEEE Symposium on
Conference_Location :
Pittsburgh, PA
ISSN :
1943-6092
Print_ISBN :
978-1-4577-1246-3
Type :
conf
DOI :
10.1109/VLHCC.2011.6070377
Filename :
6070377
Link To Document :
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