Crowds, Communities, and Mixed-Initiative Systems
In this talk, I highlight opportunities for designing new forms of crowd-supported, mixed-initiative systems that tightly integrate crowd work, community process, and intelligent user interfaces to solve complex problems that no machine nor interested party can solve alone.
I present two examples. My first example is Mobi, a system that coordinates a crowd to plan custom trip itineraries. By using automatically generated todo items to focus the crowd's attention on what needs work, Mobi illustrates a novel approach for handling human computation tasks that are difficult to decompose.
My second example is Cobi, a system that engages an entire academic community in planning a conference. Communitysourcing applications collect preferences, constraints, and affinity data from community members, and intelligent session-making and scheduling interfaces combine communitysourced data and constraint-solving to enable organizers to make informed decisions when creating and improving the schedule. I will share findings from recent deployments for planning CHI and CSCW, the two largest conferences in human computer interaction.
Haoqi Zhang is an assistant professor at Northwestern University in EECS and the Segal Design Institute. His research spans the fields of social computing, human computer interaction, artificial intelligence, and decision science. His current work focuses on engaging crowds and communities in problem solving efforts, and on advancing new data-driven design processes. He received his Ph.D. in Computer Science and B.A. in Computer Science and Economics from Harvard University.
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Contact Elizabeth Gerber at 847-467-0607 or firstname.lastname@example.org for further questions.