Decentralized collaborative work
Halls department, Hall 3
Date and Time
Wednesday, 27 December 2017
15:30 - 16:30
Distributed, parallel crowd workers can accomplish simple tasks through workflows, but teams of collaborating crowd workers are necessary for complex goals. Unfortunately, a fundamental condition for effective teams — familiarity with other members — stands in contrast to crowd work’s flexible, on-demand nature. We enable effective crowd teams with Huddler, a system for workers to assemble familiar teams even under unpredictable availability and strict time constraints. Huddler utilizes a dynamic programming algorithm to optimize for highly familiar teammates when individual availability is unknown. We first present a field experiment that demonstrates the value of familiarity for crowd teams: familiar crowd teams doubled the performance of ad-hoc (unfamiliar) teams on a collaborative task. We then report a two-week field deployment wherein Huddler enabled crowd workers to convene highly familiar teams in 18 minutes on average. This research advances the goal of supporting long-term, team-based collaborations without sacrificing the flexibility of crowd work.
Niloufar Salehi is a PhD student in computer science at Stanford and a member of the Human-Computer Interaction group. Her research focuses on designing systems for collaboration by large, diverse groups online. Her work has been published and received awards in premier venues in human-computer interaction including ACM CHI and CSCW. She has been awarded a Stanford Graduate Fellowship and a Stanford School of Engineering Fellowship. She is interested in the design of social computing systems that support collaboration and cooperation at scale. Her research approach is based heavily on social psychology and organizational behavior on the conditions for effective collaboration and cooperation. She uses this theoretical framing to understand social behavior and design technical solutions. She has designed systems that facilitate collaboration towards complex, interdependent, and innovative goals. For instance, her research has supported people with disabilities, designers, and programmers to work towards a more accessible internet through open, decentralized design.