Crowd-Powered Experts

Crowdsourcing is often applied for the task of replacing the scarce or expensive labour of experts with that of untrained workers. In this paper, we argue, that this objective might not always be desirable, but that we should instead aim at leveraging the considerable work force of the crowd in order to support the highly trained expert. Here, we demonstrate this different paradigm on the example of detecting malignant breast cancer in medical images. We compare the effectiveness and efficiency of experts to that of crowd workers, finding significantly better performance at greater cost. In a second series of experiments, we show how the comparably cheap results produced by crowdsourcing workers can serve to make experts more efficient AND more effective at the same time.

ecir2014-poster

The full version of this article has been accepted for presentation at the ECIR 2014 Workshop on Gamification for Information Retrieval (GamifIR) in Amsterdam, The Netherlands.