Browse Theses

Online crowdsourcing marketplaces provide access to millions of individuals with a range of expertise and experiences. To date, however, most research has focused on microtask platforms, such as Amazon Mechanical Turk. While microtask platforms have enabled non-expert workers to complete goals like text shortening and image labeling, highly complex and interdependent goals, such as web development and design, remain out of reach. Goals of this nature require deep knowledge of the subject matter and cannot be decomposed into independent microtasks for anyone to complete. This thesis shifts away from paid microtask work and introduces diverse expert crowds as a core component of crowdsourcing systems. Specifically, this thesis introduces and evaluates two generalizable approaches for crowdsourcing complex work with experts. The first approach, flash teams, is a framework for dynamically assembling and computationally managing crowdsourced expert teams. The second approach, flash organizations, is a framework for creating rapidly assembled and reconfigurable organizations composed of large groups of expert crowd workers. Both of these approaches for interdependent expert crowd work are manifested in Foundry, which is a computational platform we have built for authoring and managing teams of expert crowd workers. Taken together, this thesis envisions a future of work in which digitally networked teams and organizations dynamically assemble from a globally distributed online workforce and computationally orchestrate their efforts to accomplish complex work.