CS547 Human-Computer Interaction Seminar   (Seminar on People, Computers, and Design)

Fridays 11:30am-12:30pm PT · Gates B3 · Open to the public
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Dan Weld


University of Washington
High-Quality Crowdsourcing
May 19, 2017

Requestors often complain about the low-quality of crowd work, but whose fault is this? We argue that techniques like majority vote and expectation maximization (EM) miss the point and don’t solve the true, underlying problems: confusing task instructions and poor worker training. Instead we advocate three new methods: 1) gated instruction, 2) adaptive testing, 3) micro-argumentation, and 4) self-improving workflows. These methods fuse ideas from HCI with AI methods such as partially-observable Markov decision problems & reinforcement learning. As a bonus, we’ll present recent work on active learning, where the crowd does more than just label examples.




Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science & Engineering and Entrepreneurial Faculty Fellow at the University of Washington. After formative education at Phillips Academy, he received bachelor's degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator's award in 1989, an Office of Naval Research Young Investigator's award in 1990, was named AAAI Fellow in 1999 and deemed ACM Fellow in 2005. Dan was a founding editor for the Journal of AI Research, was area editor for the Journal of the ACM, guest editor for Computational Intelligence and Artificial Intelligence.