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Jennifer Wortman Vaughan: "The Communication Network Within the Crowd"
Jennifer Wortman Vaughan

Fri Dec 09 12:05 AM -- 12:55 AM (PST) @ None

Since its inception, crowdsourcing has been considered a black-box approach to solicit labor from a crowd of workers. Furthermore, the “crowd" has been viewed as a group of independent workers. Recent studies based on in-person interviews have opened up the black box and shown that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. In this talk, I will describe our attempt to quantify this discovery by mapping the entire communication network of workers on Amazon Mechanical Turk, a leading crowdsourcing platform. We executed a task in which over 10,000 workers from across the globe self-reported their communication links to other workers, thereby mapping the communication network among workers. Our results suggest that while a large percentage of workers indeed appear to be independent, there is a rich network topology over the rest of the population. That is, there is a substantial communication network within the crowd. We further examined how online forum usage relates to network topology, how workers communicate with each other via this network, how workers’ experience levels relate to their network positions, and how U.S. workers differ from international workers in their network characteristics. These findings have implications for requesters, workers, and platform providers. This talk is based on joint work with Ming Yin, Mary Gray, and Sid Suri.

Author Information

Jennifer Wortman Vaughan (Microsoft Research)
Jennifer Wortman Vaughan

Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.

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