Expert Crowdsourcing with Flash Teams
2014 CSS Fellowship
Over the last decade, web-mediated marketplaces have emerged and enabled a new type of computational labor economy that has broadened crowdsourcing from volunteerism (e.g., Wikipedia) to paid labor. To date, most crowdsourcing platforms, such as Amazon Mechanical Turk, are designed to recruit amateurs to accomplish microtasks that require little, such as document editing and translation. Current teams of crowd experts are neither effective nor efficient at completing complex interdependent tasks due to a lack of generalizable techniques for guiding expert crowds. Overcoming these challenges requires a multidisciplinary approach with contributions from both (1) behavioral scientists, such as methods and theories from organizational behavior, who understand how to manage team structures, how people are motivated and the way work is organized; and (2) computer scientists, who understand and can develop the necessary technology to investigate and extend our capability in this area. This project aims to fill this critical gap by building on research in organizational theory and computer science to develop a theoretical model and authoring platform designed to support the creation, coordination, and effectiveness of teams of interdependent expert crowds, referred to as flash teams.
Valentine, Melissa A., Daniela Retelny, Alexandra To, Negar Rahmati, Tulsee Doshi, and Michael S. Bernstein. "Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations." CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (May 2017): 3523–3537.