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 that is not encoded in the pixels or on the inference needed to apply that knowledge. 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, the sensitivity and bias distributions of these models are remarkably similar to those of human volunteers, suggesting the models' suitability for crowd-based participation. We are currently conducting studies to examine the extent to which such human/AI ensembles may give purpose to imperfect ML models as an intermediate practicable step toward fully automated solutions. Our workshop sought to motivate and communicate this approach via three talks followed by a Hybrid AI simulation game, in which workshop participants broke out into teams to design hybrid AI architectures that employ CrowdBots for existing and imagined citizen science scenarios. This workshop seeded a collection of novel information processing configurations that leverage this new approach to combining AI and human cognition.