Talk Title: Promoting Worker Performance with Human-Centered Data Science
Talk Abstract: Addressing real-world problems about human behavior is one of the main approaches where advances in data science techniques and social science theories achieve the greatest social impact. To approach these problems, we propose a human-centered data science framework that synergizes strengths across machine learning, causal inference, field experiment, and social science theories to understand, predict, and intervene in human behavior. In this talk, I will present three empirical studies that promote worker performance with human-centered data science. In the first project, we work with New York City’s Mayor’s Office and deploy explainable machine learning models to predict the risk of tenant harassment in New York City. In the second project, we leverage insights from social identity theory and conduct a large-scale field experiment on DiDi, a leading ride-sharing platform, showing that the intervention of bonus-free team ranking/contest systems can improve driver engagement. Third, to further unpack the effect of team contests on individual DiDi drivers, we bring together causal inference, machine learning, and social science theories to predict individual treatment effects. Insights from this study are directionally actionable to improve team recommender systems and contest design. More promising future directions will be discussed to showcase the effectiveness and flexibility of this framework.
Bio: I am a final-year Ph.D. candidate at the School of Information, University of Michigan, Ann Arbor, working with Professor Qiaozhu Mei. My research focuses on human-centered data science, where I couple data science techniques and social science theories to address real-world problems by understanding, predicting, and intervening in human behavior.
Specifically, I synergize strengths across machine learning, causal inference, field experiments, and social science theories to solve practical problems in the areas of data science for social good, the sharing economy, crowdsourcing, crowdfunding, social media, and health. For example, we have collaborated with the New York City’s Mayor’s Office and helped to prioritize government outreach to tenants vulnerable to landlord harassment in New York City by deploying machine learning models. In collaboration with Didi Chuxing, a leading ride-sharing platform, we have leveraged field experiments and machine learning models to enhance driver engagement and intervention design. The results of my work have been integrated into the real-world products that involve millions of users and have been published across data mining, social computing, and human-computer interaction venues.