Wei Tang

Bio: Wei is a PhD candidate in the Department of Computer Science & Engineering at the Washington University in St. Louis, advised by Chien-Ju Ho. His research interests are in online learning, algorithmic economics, optimization, and behavioral experiments, with a focus on developing theoretically rigorous, empirically grounded frameworks to understand and design human-centered algorithms. He received the B.E. degree from Tianjin University in 2017.

Talk Title: Learning with Understanding: Human Behavior in Algorithm Design

Talk Abstract: Algorithms increasingly pervade every sphere of human life and thus have great potential to reshape various sectors of our modern society. Thus, it is important to understand the role humans play in the design of algorithm. However, human involvement also creates unique challenges. Humans might be careless, strategic, or have behavioral biases.
In this talk, I will present two works from my own research on theoretically and empirically dealing with these challenges when humans are involved in algorithm design. First, I will describe the problem on learning with human biased behavior. In this problem, the learner cannot directly observe the realized reward of an action but can only observe human biased feedback on the realized reward. I explored two natural human feedback models. Our results show that a small deviation on user behavior model and/or the design of the information structure could significant impact the overall system outcome.
I then step back and examine whether the standard behavior models capture human behavior in practice by utilizing behavioral experiments. I studied this question in AI-assisted decision-making where AI intelligently abstracts out useful information from a large amount of data. Human then review the information output by the AI and make the decision. I have run behavior experiments to characterize human’s response in practice and established an empirically grounded human behavior model.