AAAI Publications, First AAAI Conference on Human Computation and Crowdsourcing

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HiveMind: Tuning Crowd Response with a Single Value
Preetjot Singh, Walter S. Lasecki, Paulo Barelli, Jeffrey P. Bigham

Last modified: 2013-11-03

Abstract


One common problem plaguing crowdsourcing tasks is tuning the set of worker responses: Depending on task requirements, requesters may want a large set of rich and varied worker responses (typically in subjective evaluation tasks) or a more convergent response-set (typically for more objective tasks such as fact-checking). This problem is especially salient in tasks that combine workers’ responses to present a single output: Divergence in these settings could either add richness and complexity to the unified answer, or noise. In this paper we present HiveMind, a system of methods that allow requesters to tune different levels of convergence in worker participation for different tasks simply by adjusting the value of one variable.


Keywords


human computation, incentives, human computer interaction, mechanism design, game theory, incentive model

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