Incentivizing Societal Contributions for and via Machine Learning

Speaker Name: 
Yang Liu
Speaker Title: 
Postdoctoral Fellow
Speaker Organization: 
Harvard University
Start Time: 
Wednesday, March 14, 2018 - 11:00am
End Time: 
Wednesday, March 14, 2018 - 12:15pm
Location: 
E2-506
Organizer: 
Lise Getoor

Abstract:

Machine learning (ML) and automatic algorithmic decision making have started to play central and crucial roles in our daily lives. At the same time, more and more data used to train ML algorithms are now collected through crowdsourcing or other forms of participatory computation involving people. With people being both the source and the ultimate target of these algorithms, which are increasingly being used to assist in making important and sometimes life-changing decisions, new and interesting challenges arise.

I will present our studies that address the challenges of incentivizing societal contributions for building better and more robust ML algorithms. In the first part of the talk, I will demonstrate how ML techniques can be leveraged to quantify the value of human reported information when there is no ground-truth verification. I show how these results help design better incentive mechanisms to encourage user input and help make high quality data collection more efficiently, compared to existing, non-ML based methods. In the second part, I will show how the Multi-Armed Bandit type of techniques can help resolve above data collection problem in a sequential setting. I will conclude my talk with future works.

Bio: 

Yang Liu is currently a postdoctoral fellow at Harvard University. He obtained his PhD degree from the Department of EECS, University of Michigan Ann Arbor in 2015.  He also obtained a Master of Science in EE:Systems and in Mathematics in 2012 and 2014 respectively, both from University of Michigan, and holds a Bachelor degree from Shanghai Jiao Tong University, China. His research interests broadly focus on the interactions between society and artificial intelligence (AI), and in particular algorithmic decision-making. He was a Finalist for the Towner Prize (at Michigan) for Outstanding Ph.D. research in 2015. His was the winner of the best poster award at Michigan Engineering Symposium in 2011 and the best application paper award at the IEEE International Conference on Data Science and Advanced Analytics in 2014.