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Crowdsourcing: Beyond Label Generation
Jennifer Wortman Vaughan

Sun Dec 04 11:30 PM -- 01:30 AM (PST) @ Area 3

This tutorial will showcase some of the most innovative uses of crowdsourcing that have emerged in the past few years. While some have clear and immediate benefits to machine learning, we will also discuss examples in which crowdsourcing has allowed researchers to answer exciting questions in psychology, economics, and other fields.

We will discuss best practices for crowdsourcing (such as how and why to maintain a positive relationship with crowdworkers) and available crowdsourcing tools. We will survey recent research examining the effect of incentives on crowdworker performance. Time permitting, we will also touch on recent ethnographic research studying the community of crowdworkers and/or delve into the ethical implications of crowdsourcing.

Despite the inclusion of best practices and tools, this tutorial should not be viewed as a prescriptive guide for applying existing techniques. The goals of the tutorial are to inspire you to find novel ways of using crowdsourcing in your own research and to provide you with the resources you need to avoid common pitfalls when you do.

Target audience: This tutorial is open to anyone who wants to learn more about cutting edge research in crowdsourcing. No assumptions will be made about the audience's familiarity with either crowdsourcing or specific machine learning techniques. Anyone who is curious is welcome to attend!

As the tutorial approaches, more information will be available on the tutorial website: http://www.jennwv.com/projects/crowdtutorial.html

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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