Computer Science Seminar by Zhuoran Lu: Accounting Individual Cognition and Social Influence for Better Human-AI Collaboration
Speaker: , Ph.D. candidate, Purdue University
Title: Accounting Individual Cognition and Social Influence for Better Human-AI Collaboration
Abstract: Artificial intelligence (AI) technologies are increasingly integrated into our lives, enabling a new paradigm of human-AI decision-making where AI provides recommendations, and humans make the final decision. However, such human-AI collaboration often yields mixed results in reality, highlighting the need to understand how humans engage with AI-based systems and optimize human-AI collaboration's effectiveness. In this talk, I address this challenge from two perspectives. First, I explore how individual cognitive processes shape human-AI interactions and how these insights can be modeled and incorporated into the design of AI models optimized for human-AI teams. Second, I examine the role of social influence—information humans receive beyond AI recommendations—in decision-making and discuss how it can be strategically designed in AI-based systems to foster critical reflections of AI recommendations and enhance human-AI collaboration.
Bio:
is a Ph.D. candidate in Computer Science at Purdue University, advised by Prof. Ming Yin. His research lies at the intersection of human-computer interaction (HCI) and artificial intelligence (AI), with a focus on human-AI interaction, human-centered AI, and applied machine learning. His work has been published in top-tier venues across HCI (e.g., CHI, CSCW) and AI (e.g., AAAI, IJCAI), and was recognized with the Best Paper Award at CSCW 2022. His research has been supported by the Frederick N. Andrews Fellowship and the Bilsland Dissertation Fellowship from Purdue University.