Human Intelligence Needs Artificial Intelligence.

Crowdsourcing platforms, such as Amazon Mechanical Turk, have enabled the construction of scalable applications for tasks ranging from product categorization and photo tagging to audio transcription and translation. These vertical applications are typically realized with complex, self-managing workflows that guarantee quality results. But constructing such workflows is challenging, with a huge number of alternative decisions for the designer to consider. We argue the thesis that "Artificial intelligence methods can greatly simplify the process of creating and managing complex crowdsourced workflows." We present the design of CLOWDER, which uses machine learning to continually refine models of worker performance and task difficulty. Using these models, CLOWDER uses decision-theoretic optimization to 1) choose between alternative workflows, 2) optimize parameters for a workflow, 3) create personalized interfaces for individual workers, and 4) dynamically control the workflow. Preliminary experience suggests that these optimized workflows are significantly more economical (and return higher quality output) than those generated by humans.