Facilitating Deliberation with Real-time Crowd Workers For Better Prediction on Car Accident

Instantaneous crowdsourcing, which prefetches possible future states and receiving crowd task results ahead the critical events really happens, introduced much potential to solve real-world problems that require accelerated human capabilities. However, real-world is complex, and there can be a magnificent amount of possible future states. Though humans are capable of predicting critical events, because uncertainties in predicting the future, their predictions can be either diverging or wrong. Crowdsourcing research has been shown the effectiveness of collective deliberation in such problems, but it can be limited in problems that only allows limited time for workers to do the task. We introduce efficient and quick collaboration techniques that can induce crowd workers to deliberate and do tasks better in real-time. We test our technique in the context of predicting car accidents.

Retrieving Collective Distribution of Crowd Annotation by Deliberative Estimation on How Others Would Annotate

Crowdsourcing has been widely used for labeling categories of data. It has tried to aggregate varied and error-prone responses into one accurate category. However, some data like the audio-visual expression of the emotion can be interpreted in diverse ways. For such data, the overall perception of the crowd on the data can convey how diverse interpretations are possible. However, how to collect such distribution of the perception efficiently is under-explored and naive crowdsourcing method can be expensive to collect the distribution. In this paper, we explore how we can crowdsource the perception of the overall crowd efficiently by making workers interact each other and estimate how other people would annotate the data. We introduce a workflow that not only make workers converge in their estimation by collaborative deliberation, but also reconsider neglected options by deliberative prompting.