Measure and Manage Trust in Human-AI Conversations

Mengyao Li

This talk is part of the GVU Brown Bag Seminar Series brought to you by the Institute for People and Technology at Georgia Tech.

Speaker: Mengyao Li, Ph.D., Assistant Professor in the School of Psychology at Georgia Tech

Date: 2023-10-12 12:30 pm

Technology Square Research Building (TSRB, 1st Floor Ballroom)
85 Fifth Street NW
Atlanta, GA 30308

Artificial Intelligence (AI), with its increasing capability and connectivity, extends beyond limited and well-defined contexts and is integrated into broader societal domains. AI algorithms now steer autonomous vehicle fleets, shape political beliefs through news filtering, and oversee resource allocation and labor. Establishing trust between humans and their AI counterparts becomes important to facilitate effective cooperation. Trust profoundly influences how individuals use, communicate with, and collaborate alongside AI systems. Thus, trust measurement and management within human-AI cooperation are indispensable for ensuring safety, efficiency, and overall success. This talk focuses on trust in human-AI interactions, addressing three primary questions: (1) How can we measure people’s trust in human-AI conversations? (2) How does trust change over time within human-AI conversations? (3) How can we effectively manage instances of overtrust or undertrust through conversational cues to enhance human-AI cooperation? This talk highlights critical advancements in measurement of trust dynamics in human-AI cooperation, promising to influence the future of AI integration into broader societal domains.

Mengyao Li an Assistant Professor in the School of Psychology at Georgia Tech. She received her Ph.D. in Industrial & System Engineering from the University of Wisconsin–Madison. She directs the Hybrid Intelligence Lab (HI lab), which explores the interaction of human and machine intelligence. Her research is concentrated on understanding, predicting, and shaping human-AI communication, social cooperation, and the long-term coevolution within safety-critical environments.