Crowdsourcing is an increasingly powerful method for combining amateurs' efforts to recreate an expert's abilities. However, across domains from design to engineering to art, few goals are truly the effort of just one person — even one expert. If we can now crowdsource simple tasks such as image labeling, how might we coordinate many peoples' abilities toward far more complex and interdependent goals? In this talk, I present computational systems for gathering and guiding crowds of experts --- including professional programmers, designers, singers and artists. The resulting collectives tackle problems modularly and at scale, dynamically grow and shrink depending on task demands, and combine into larger organizations. I'll demonstrate how these expert crowds, which we call Flash Teams, can pursue goals such as designing new user experiences overnight and producing animated shorts in two days.
As new forms of online work such as Flash Teams emerge, new forms of digital labor challenges also arise. I will introduce our work creating an online platform to facilitate collective action for workers on the Amazon Mechanical Turk marketplace. Our reflection from this effort: the nature of online communities make it easy to form new forms of publics to form, but equally easy for them to stall, face internal friction, and ultimately disintegrate.
Michael Bernstein is an Assistant Professor of Computer Science at Stanford University, where he co-directs the Human-Computer Interaction group and is a Robert N. Noyce Family Faculty Scholar. His research in human-computer interaction focuses on the design of crowdsourcing and social computing systems. This work has received Best Paper awards and nominations at premier venues in human-computer interaction and social computing (ACM UIST, ACM CHI, ACM CSCW, AAAI ISWSM). Michael has been recognized with the NSF CAREER award, as well as the George M. Sprowls Award for best doctoral thesis in Computer Science at MIT. He holds Ph.D. and M.S. degrees in Computer Science from MIT, and a B.S. in Symbolic Systems from Stanford University.