Abstract
Crowdsourcing is increasingly gaining attention as one of the most promising forms of large-scale dynamic collective work. However current crowdsourcing approaches do not offer guarantees often demanded by consumers, for example regarding minimum quality, maximum cost or job accomplishment time. The problem appears to have a greater impact in corporate environments because in this case the above-mentioned performance guarantees directly affect its viability against competition. Guided crowdsourcing can be an alternative to overcome these issues. Guided crowdsourcing refers to the use of Artificial Intelligence methods to coordinate workers in crowdsourcing settings, in order to ensure collective performance goals such as quality, cost or time. In this paper, we investigate its potential and examine it on an evaluation setting tailored for intra and inter-corporate environments.
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Lykourentzou, I., Vergados, D.J., Papadaki, K., Naudet, Y. (2013). Guided Crowdsourcing for Collective Work Coordination in Corporate Environments. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_10
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DOI: https://doi.org/10.1007/978-3-642-40495-5_10
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