Preparing complex jobs for crowdsourcing marketplaces requires careful attention to workflow design, the process of decomposing jobs into multiple tasks, which are solved by multiple workers. Can the crowd help design such workflows? This paper presents Turkomatic, a tool that recruits crowd workers to aid requesters in planning and solving complex jobs. While workers decompose and solve tasks, requesters can view the status of worker-designed workflows in real time; intervene to change tasks and solutions; and request new solutions to subtasks from the crowd. These features lower the threshold for crowd employers to request complex work. During two evaluations, we found that allowing the crowd to plan without requester supervision is partially successful, but that requester intervention during workflow planning and execution improves quality substantially. We argue that Turkomatic's collaborative approach can be more successful than the conventional workflow design process and discuss implications for the design of collaborative crowd planning systems.

Completing complex tasks on crowdsourcing platforms like Mechanical Turk currently requires significant upfront investment into task decomposition and workflow design. We present a new method for automating task and workflow design for high-level, complex tasks. Unlike previous approaches, our strategy is recursive, recruiting workers from the crowd to help plan out how problems can be solved most effectively. Our initial experiments suggest that this strategy can successfully create workflows to solve tasks considered difficult from an AI perspective, although it is highly sensitive to the design choices made by workers.