Combining human and machine intelligence in large-scale crowdsourcing

摘要: We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans computational agents solve crowdsourcing tasks. construct a set Bayesian predictive models from data describe operate within an overall crowd-sourcing architecture that combines efforts people vision on task classifying celestial bodies defined citizens' science project named Galaxy Zoo. learned probabilistic used fuse human contributions predict behaviors workers. employ multiple inferences in concert guide decisions hiring routing workers tasks so as maximize efficiency large-scale processes based expected utility.