We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowdsourcing architecture that combines the e\ufb00orts of people and machine vision on the task of classifying celestial bodies de\ufb01ned within a citizens\u2019 science project named Galaxy Zoo. We show how learned probabilistic models can be used to fuse human and machine contributions and to predict the behaviors of workers. We employ multiple inferences in concert to guide decisions on hiring and routing workers to tasks so as to maximize the e\ufb03ciency of large-scale crowdsourcing processes based on expected utility.<\/p>\n","protected":false},"excerpt":{"rendered":"
We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowdsourcing architecture that combines the e\ufb00orts of people and machine vision on 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