Exploring CrowdBots: a new evolutionary pathway for citizen science projects

Collective hybrid intelligence may serve as an effective progression in the evolution of crowd-powered systems such as citizen science projects, which rely on human cognition. Supervised machine learning has been treated as a panacea for automated image classification under the assumption that prediction performance depends primarily on the quality and volume of the training corpus, which is often obtained through crowdsourcing. We have observed that this assumption may fall short when accurate classification depends upon contextual knowledge and abstraction capabilities. We organized a machine learning competition using data previously analysed by humans on our “Stall Catchers” citizen science platform, which gave rise to models exhibiting a range of performance characteristics. Though none of these models exhibits classification performance sufficient to replace Stall Catchers